1885 Data Engineering Jobs - Page 33

Job Overview We are looking for a Data Engineer who will be part of our Analytics Practice and will be expected to actively work in a multi-disciplinary fast paced environment. This role requires a broad range of skills and the ability to step into different roles depending on the size and scope of the project; its primary responsibility is the acquisition, transformation, loading and processing of data from a multitude of disparate data sources, including structured and unstructured data for advanced analytics and machine learning in a big data environment. Responsibilities: Engineer a modern data pipeline to collect, organize, and process data from disparate sources. Performs data management tasks, such as conduct data profiling, assess data quality, and write SQL queries to extract and integrate data Develop efficient data collection systems and sound strategies for getting quality data from different sources Consume and analyze data from the data pool to support inference, prediction and recommendation of actionable insights to support business growth. Design and develop ETL processes using tools and scripting. Troubleshoot and debug ETL processes. Performance tuning and opitimization of the ETL processes. Provide support to new of existing applications while recommending best practices and leading projects to implement new functionality. Collaborate in design reviews and code reviews to ensure standards are met. Recommend new standards for visualizations. Learn and develop new ETL techniques as required to keep up with the contemporary technologies. Reviews the solution requirements and architecture to ensure selection of appropriate technology, efficient use of resources and integration of multiple systems and technology. Support presentations to Customers and Partners Advising on new technology trends and possible adoption to maintain competitive advantage Experience Needed: 8+ years of related experience is required. A BS or Masters degree in Computer Science or related technical discipline is required ETL experience with data integration to support data marts, extracts and reporting Experience connecting to varied data sources Excellent SQL coding experience with performance optimization for data queries. Understands different data models like normalized, de-normalied, stars, and snowflake models. Worked with transactional, temporarl, time series, and structured and unstructured data. Experience on Azure Data Factory and Azure Synapse Analytics Worked in big data environments, cloud data stores, different RDBMS and OLAP solutions. Experience in cloud-based ETL development processes. Experience in deployment and maintenance of ETL Jobs. Is familiar with the principles and practices involved in development and maintenance of software solutions and architectures and in service delivery. Has strong technical background and remains evergreen with technology and industry developments. At least 3 years of demonstrated success in software engineering, release engineering, and/or configuration management. Highly skilled in scripting languages like PowerShell. Substantial experience in the implementation and exectuion fo CI/CD processes. Additional Demonstrated ability to have successfully completed multiple, complex technical projects Prior experience with application delivery using an Onshore/Offshore model Experience with business processes across multiple Master data domains in a services based company Demonstrates a rational and organized approach to the tasks undertaken and an awareness of the need to achieve quality. Demonstrates high standards of professional behavior in dealings with clients, colleagues and staff. Is able to make sound and far reaching decisions alone on major issues and to take full responsibility for them on a technical basis. Strong written communication skills. Is effective and persuasive in both written and oral communication. Experience with gathering end user requirements and writing technical documentation Time management and multitasking skills to effectively meet deadlines under time-to-market pressure

Job Area: Information Technology Group, Information Technology Group > IT Data Engineer General Summary: Developer will play an integral role in the PTEIT Machine Learning Data Engineering team. Design, develop and support data pipelines in a hybrid cloud environment to enable advanced analytics. Design, develop and support CI/CD of data pipelines and services. - 5+ years of experience with Python or equivalent programming using OOPS, Data Structures and Algorithms - Develop new services in AWS using server-less and container-based services. - 3+ years of hands-on experience with AWS Suite of services (EC2, IAM, S3, CDK, Glue, Athena, Lambda, RedShift, Snowflake, RDS) - 3+ years of expertise in scheduling data flows using Apache Airflow - 3+ years of strong data modelling (Functional, Logical and Physical) and data architecture experience in Data Lake and/or Data Warehouse - 3+ years of experience with SQL databases - 3+ years of experience with CI/CD and DevOps using Jenkins - 3+ years of experience with Event driven architecture specially on Change Data Capture - 3+ years of Experience in Apache Spark, SQL, Redshift (or) Big Query (or) Snowflake, Databricks - Deep understanding building the efficient data pipelines with data observability, data quality, schema drift, alerting and monitoring. - Good understanding of the Data Catalogs, Data Governance, Compliance, Security, Data sharing - Experience in building the reusable services across the data processing systems. - Should have the ability to work and contribute beyond defined responsibilities - Excellent communication and inter-personal skills with deep problem-solving skills. Minimum Qualifications: 3+ years of IT-related work experience with a Bachelor's degree in Computer Engineering, Computer Science, Information Systems or a related field. OR 5+ years of IT-related work experience without a Bachelors degree. 2+ years of any combination of academic or work experience with programming (e.g., Java, Python). 1+ year of any combination of academic or work experience with SQL or NoSQL Databases. 1+ year of any combination of academic or work experience with Data Structures and algorithms. 5 years of Industry experience and minimum 3 years experience in Data Engineering development with highly reputed organizations- Proficiency in Python and AWS- Excellent problem-solving skills- Deep understanding of data structures and algorithms- Proven experience in building cloud native software preferably with AWS suit of services- Proven experience in design and develop data models using RDBMS (Oracle, MySQL, etc.) Desirable - Exposure or experience in other cloud platforms (Azure and GCP) - Experience working on internals of large-scale distributed systems and databases such as Hadoop, Spark - Working experience on Data Lakehouse platforms (One House, Databricks Lakehouse) - Working experience on Data Lakehouse File Formats (Delta Lake, Iceberg, Hudi) Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.

Job Area: Engineering Group, Engineering Group > Software Engineering General Summary: As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Software Engineer, you will design, develop, create, modify, and validate embedded and cloud edge software, applications, and/or specialized utility programs that launch cutting-edge, world class products that meet and exceed customer needs. Qualcomm Software Engineers collaborate with systems, hardware, architecture, test engineers, and other teams to design system-level software solutions and obtain information on performance requirements and interfaces. Minimum Qualifications: Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field. 2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc. General Summary Preferred Qualifications 3+ years of experience as a Data Engineer or in a similar role Experience with data modeling, data warehousing, and building ETL pipelines Solid working experience with Python, AWS analytical technologies and related resources (Glue, Athena, QuickSight, SageMaker, etc.,) Experience with Big Data tools , platforms and architecture with solid working experience with SQL Experience working in a very large data warehousing environment, Distributed System. Solid understanding on various data exchange formats and complexities Industry experience in software development, data engineering, business intelligence, data science, or related field with a track record of manipulating, processing, and extracting value from large datasets Strong data visualization skills Basic understanding of Machine Learning; Prior experience in ML Engineering a plus Ability to manage on-premises data and make it inter-operate with AWS based pipelines Ability to interface with Wireless Systems/SW engineers and understand the Wireless ML domain; Prior experience in Wireless (5G) domain a plus Education Bachelor's degree in computer science, engineering, mathematics, or a related technical discipline Preferred QualificationsMasters in CS/ECE with a Data Science / ML Specialization Minimum Qualifications: Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field OR PhD in Engineering, Information Systems, Computer Science, or related field. 3+ years of experience with Programming Language such as C, C++, Java, Python, etc. Develops, creates, and modifies general computer applications software or specialized utility programs. Analyzes user needs and develops software solutions. Designs software or customizes software for client use with the aim of optimizing operational efficiency. May analyze and design databases within an application area, working individually or coordinating database development as part of a team. Modifies existing software to correct errors, allow it to adapt to new hardware, or to improve its performance. Analyzes user needs and software requirements to determine feasibility of design within time and cost constraints. Confers with systems analysts, engineers, programmers and others to design system and to obtain information on project limitations and capabilities, performance requirements and interfaces. Stores, retrieves, and manipulates data for analysis of system capabilities and requirements. Designs, develops, and modifies software systems, using scientific analysis and mathematical models to predict and measure outcome and consequences of design. Principal Duties and Responsibilities: Completes assigned coding tasks to specifications on time without significant errors or bugs. Adapts to changes and setbacks in order to manage pressure and meet deadlines. Collaborates with others inside project team to accomplish project objectives. Communicates with project lead to provide status and information about impending obstacles. Quickly resolves complex software issues and bugs. Gathers, integrates, and interprets information specific to a module or sub-block of code from a variety of sources in order to troubleshoot issues and find solutions. Seeks others' opinions and shares own opinions with others about ways in which a problem can be addressed differently. Participates in technical conversations with tech leads/managers. Anticipates and communicates issues with project team to maintain open communication. Makes decisions based on incomplete or changing specifications and obtains adequate resources needed to complete assigned tasks. Prioritizes project deadlines and deliverables with minimal supervision. Resolves straightforward technical issues and escalates more complex technical issues to an appropriate party (e.g., project lead, colleagues). Writes readable code for large features or significant bug fixes to support collaboration with other engineers. Determines which work tasks are most important for self and junior engineers, stays focused, and deals with setbacks in a timely manner. Unit tests own code to verify the stability and functionality of a feature.

Role Purpose The purpose of this role is to design, test and maintain software programs for operating systems or applications which needs to be deployed at a client end and ensure its meet 100% quality assurance parameters Responsibilities: Design and implement the data modeling, data ingestion and data processing for various datasets Design, develop and maintain ETL Framework for various new data source Develop data ingestion using AWS Glue/ EMR, data pipeline using PySpark, Python and Databricks. Build orchestration workflow using Airflow & databricks Job workflow Develop and execute adhoc data ingestion to support business analytics. Proactively interact with vendors for any questions and report the status accordingly Explore and evaluate the tools/service to support business requirement Ability to learn to create a data-driven culture and impactful data strategies. Aptitude towards learning new technologies and solving complex problem. Qualifications: Minimum of bachelors degree. Preferably in Computer Science, Information system, Information technology. Minimum 5 years of experience on cloud platforms such as AWS, Azure, GCP. Minimum 5 year of experience in Amazon Web Services like VPC, S3, EC2, Redshift, RDS, EMR, Athena, IAM, Glue, DMS, Data pipeline & API, Lambda, etc. Minimum of 5 years of experience in ETL and data engineering using Python, AWS Glue, AWS EMR /PySpark and Airflow for orchestration. Minimum 2 years of experience in Databricks including unity catalog, data engineering Job workflow orchestration and dashboard generation based on business requirements Minimum 5 years of experience in SQL, Python, and source control such as Bitbucket, CICD for code deployment. Experience in PostgreSQL, SQL Server, MySQL & Oracle databases. Experience in MPP such as AWS Redshift, AWS EMR, Databricks SQL warehouse & compute cluster. Experience in distributed programming with Python, Unix Scripting, MPP, RDBMS databases for data integration Experience building distributed high-performance systems using Spark/PySpark, AWS Glue and developing applications for loading/streaming data into Databricks SQL warehouse & Redshift. Experience in Agile methodology Proven skills to write technical specifications for data extraction and good quality code. Experience with big data processing techniques using Sqoop, Spark, hive is additional plus Experience in data visualization tools including PowerBI, Tableau. Nice to have experience in UI using Python Flask framework anglular Mandatory Skills: Python for Insights. Experience: 5-8 Years.

Role Purpose The purpose of this role is to design, test and maintain software programs for operating systems or applications which needs to be deployed at a client end and ensure its meet 100% quality assurance parameters Do 1. Instrumental in understanding the requirements and design of the product/ software Develop software solutions by studying information needs, studying systems flow, data usage and work processes Investigating problem areas followed by the software development life cycle Facilitate root cause analysis of the system issues and problem statement Identify ideas to improve system performance and impact availability Analyze client requirements and convert requirements to feasible design Collaborate with functional teams or systems analysts who carry out the detailed investigation into software requirements Conferring with project managers to obtain information on software capabilities 2. Perform coding and ensure optimal software/ module development Determine operational feasibility by evaluating analysis, problem definition, requirements, software development and proposed software Develop and automate processes for software validation by setting up and designing test cases/scenarios/usage cases, and executing these cases Modifying software to fix errors, adapt it to new hardware, improve its performance, or upgrade interfaces. Analyzing information to recommend and plan the installation of new systems or modifications of an existing system Ensuring that code is error free or has no bugs and test failure Preparing reports on programming project specifications, activities and status Ensure all the codes are raised as per the norm defined for project / program / account with clear description and replication patterns Compile timely, comprehensive and accurate documentation and reports as requested Coordinating with the team on daily project status and progress and documenting it Providing feedback on usability and serviceability, trace the result to quality risk and report it to concerned stakeholders 3. Status Reporting and Customer Focus on an ongoing basis with respect to project and its execution Capturing all the requirements and clarifications from the client for better quality work Taking feedback on the regular basis to ensure smooth and on time delivery Participating in continuing education and training to remain current on best practices, learn new programming languages, and better assist other team members. Consulting with engineering staff to evaluate software-hardware interfaces and develop specifications and performance requirements Document and demonstrate solutions by developing documentation, flowcharts, layouts, diagrams, charts, code comments and clear code Documenting very necessary details and reports in a formal way for proper understanding of software from client proposal to implementation Ensure good quality of interaction with customer w.r.t. e-mail content, fault report tracking, voice calls, business etiquette etc Timely Response to customer requests and no instances of complaints either internally or externally Deliver No. Performance Parameter Measure 1. Continuous Integration, Deployment & Monitoring of Software 100% error free on boarding & implementation, throughput %, Adherence to the schedule/ release plan 2. Quality & CSAT On-Time Delivery, Manage software, Troubleshoot queries,Customer experience, completion of assigned certifications for skill upgradation 3. MIS & Reporting 100% on time MIS & report generation Mandatory Skills: DataBricks - Data Engineering. Experience: 3-5 Years.

ABOUT THE ROLE Role Description: As part of the cybersecurity organization, the Data Engineer is responsible for designing, building, and maintaining data infrastructure to support data-driven decision-making. This role involves working with large datasets, developing reports, executing data governance initiatives, and ensuring data is accessible, reliable, and efficiently managed. The ideal candidate has strong technical skills, experience with big data technologies, and a deep understanding of data architecture, ETL processes, and cybersecurity data frameworks. Roles & Responsibilities: Design, develop, and maintain data solutions for data generation, collection, and processing. Be a key team member that assists in design and development of the data pipeline. Create data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems. Schedule and manage workflows the ensure pipelines run on schedule and are monitored for failures. Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs. Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency. Implement data security and privacy measures to protect sensitive data. Leverage cloud platforms (AWS preferred) to build scalable and efficient data solutions. Collaborate and communicate effectively with product teams. Collaborate with data scientists to develop pipelines that meet dynamic business needs. Share and discuss findings with team members practicing SAFe Agile delivery model. Functional Skills: Basic Qualifications: Masters degree and 1 to 3 years of Computer Science, IT or related field experience OR Bachelors degree and 3 to 5 years of Computer Science, IT or related field experience OR Diploma and 7 to 9 years of Computer Science, IT or related field experience Preferred Qualifications: Hands on experience with data practices, technologies, and platforms, such as Databricks, Python, Gitlab, LucidChart,etc. Proficiency in data analysis tools (e.g. SQL) and experience with data sourcing tools Excellent problem-solving skills and the ability to work with large, complex datasets Understanding of data governance frameworks, tools, and best practices Knowledge of and experience with data standards (FAIR) and protection regulations and compliance requirements (e.g., GDPR, CCPA) Good-to-Have Skills: Experience with ETL tools and various Python packages related to data processing, machine learning model development Strong understanding of data modeling, data warehousing, and data integration concepts Knowledge of Python/R, Databricks, cloud data platforms Experience working in Product team's environment Experience working in an Agile environment Professional Certifications: AWS Certified Data Engineer preferred Databricks Certificate preferred Soft Skills: Initiative to explore alternate technology and approaches to solving problems Skilled in breaking down problems, documenting problem statements, and estimating efforts Excellent analytical and troubleshooting skills Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation Ability to manage multiple priorities successfully Team-oriented, with a focus on achieving team goals

Join our innovative team and architect the future of data solutions on Azure, Synapse, and Databricks! Senior Data Engineer (Data Architect) Additional Details: Notice Period: 30 days (maximum) Location: Remote About the Role Design and implement scalable data pipelines, data warehouses, and data lakes that drive business growth. Collaborate with stakeholders to deliver data-driven insights and shape the data landscape. Requirements 8+ years of experience in data engineering and data architecture Strong expertise in Azure services (Synapse Analytics, Databricks, Storage, Active Directory) Proven experience in designing and implementing data pipelines, data warehouses, and data lakes Strong understanding of data governance, data quality, and data security Experience with infrastructure design and implementation, including DevOps practices and tools

We are seeking an experienced Senior Data Engineer to join our team. The ideal candidate will have a strong background in data engineering and AWS infrastructure, with hands-on experience in building and maintaining data pipelines and the necessary infrastructure components. The role will involve using a mix of data engineering tools and AWS services to design, build, and optimize data architecture. Key Responsibilities: Design, develop, and maintain data pipelines using Airflow and AWS services. Implement and manage data warehousing solutions with Databricks and PostgreSQL. Automate tasks using GIT / Jenkins. Develop and optimize ETL processes, leveraging AWS services like S3, Lambda, AppFlow, and DMS. Create and maintain visual dashboards and reports using Looker. Collaborate with cross-functional teams to ensure smooth integration of infrastructure components. Ensure the scalability, reliability, and performance of data platforms. Work with Jenkins for infrastructure automation. Technical and functional areas of expertise: Working as a senior individual contributor on a data intensive project Strong experience in building high performance, resilient & secure data processing pipelines preferably using Python based stack. Extensive experience in building data intensive applications with a deep understanding of querying and modeling with relational databases preferably on time-series data. Intermediate proficiency in AWS services (S3, Airflow) Proficiency in Python and PySpark Proficiency with ThoughtSpot or Databricks. Intermediate proficiency in database scripting (SQL) Basic experience with Jenkins for task automation Nice to Have : Intermediate proficiency in data analytics tools (Power BI / Tableau / Looker / ThoughSpot) Experience working with AWS Lambda, Glue, AppFlow, and other AWS transfer services. Exposure to PySpark and data automation tools like Jenkins or CircleCI. Familiarity with Terraform for infrastructure-as-code. Experience in data quality testing to ensure the accuracy and reliability of data pipelines. Proven experience working directly with U.S. client stakeholders. Ability to work independently and take the lead on tasks. Education and experience: Bachelors or masters in computer science or related fields. 5+ years of experience Stack/Skills needed: Databricks PostgreSQL Python & Pyspark AWS Stack Power BI / Tableau / Looker / ThoughSpot Familiarity with GIT and/or CI/CD tools

Hiring for Denodo Admin with 3+ years experience with below skills: Must Have: - Denodo admin logical data models, views & caching - ETL pipelines (Informatica/Talend) for EDW/data lakes, performance issues - SQL, Informatica, Talend, Big Data, Hive Required Candidate profile - Design, develop & maintain ETL pipelines using Informatica PowerCenter or Talend to extract, Hive - Optimize & troubleshoot complex SQL queries - Immediate Joiner is plus - Work from office is must

What you will do In this vital role you will be responsible to design, develop, and optimize data pipelines, data integration frameworks, and metadata-driven architectures that enable seamless data access and analytics. This role prefers deep expertise in big data processing, distributed computing, data modeling, and governance frameworks to support self-service analytics, AI-driven insights, and enterprise-wide data management. Design, develop, and maintain complex ETL/ELT data pipelines in Databricks using PySpark, Scala, and SQL to process large-scale datasets Understand the biotech/pharma or related domains & build highly efficient data pipelines to migrate and deploy complex data across systems Design and Implement solutions to enable unified data access, governance, and interoperability across hybrid cloud environments Ingest and transform structured and unstructured data from databases (PostgreSQL, MySQL, SQL Server, MongoDB etc.), APIs, logs, event streams, images, pdf, and third-party platforms Ensuring data integrity, accuracy, and consistency through rigorous quality checks and monitoring Expert in data quality, data validation and verification frameworks Innovate, explore and implement new tools and technologies to enhance efficient data processing Proactively identify and implement opportunities to automate tasks and develop reusable frameworks Work in an Agile and Scaled Agile (SAFe) environment, collaborating with cross-functional teams, product owners, and Scrum Masters to deliver incremental value Use JIRA, Confluence, and Agile DevOps tools to manage sprints, backlogs, and user stories Support continuous improvement, test automation, and DevOps practices in the data engineering lifecycle Collaborate and communicate effectively with the product teams, with cross-functional teams to understand business requirements and translate them into technical solutions What we expect of you We are all different, yet we all use our unique contributions to serve patients. We are looking for highly motivated expert Data Engineer who can own the design & development of complex data pipelines, solutions and frameworks. Basic Qualifications: Masters degree and 1 to 3 years of Computer Science, IT or related field experience OR Bachelors degree and 3 to 5 years of Computer Science, IT or related field experience OR Diploma and 7 to 9 years of Computer Science, IT or related field experience Hands-on experience in data engineering technologies such as Databricks, PySpark, SparkSQL Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies Proficiency in workflow orchestration, performance tuning on big data processing Strong understanding of AWS services Ability to quickly learn, adapt and apply new technologies Strong problem-solving and analytical skills Excellent communication and teamwork skills Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices Preferred Qualifications: AWS Certified Data Engineer preferred Databricks Certificate preferred Scaled Agile SAFe certification preferred Data Engineering experience in Biotechnology or pharma industry Experience in writing APIs to make the data available to the consumers Experienced with SQL/NOSQL database, vector database for large language models Experienced with data modeling and performance tuning for both OLAP and OLTP databases Experienced with software engineering best-practices, including but not limited to version control (Git, Subversion, etc.), CI/CD (Jenkins, Maven etc.), automated unit testing, and DevOps Soft Skills: Excellent analytical and troubleshooting skills. Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented, with a focus on achieving team goals. Ability to learn quickly, be organized and detail oriented. Strong presentation and public speaking skills.

ABOUT THE ROLE Role Description: We are seeking a Senior Data Engineering Manager with a strong background in Regulatory or Integrated Product Teams within the Biotech or Pharmaceutical domain. This role will lead the end-to-end data strategy and execution for regulatory product submissions, lifecycle management, and compliance reporting, ensuring timely and accurate delivery of regulatory data assets across global markets. You will be embedded in a cross-functional Regulatory Integrated Product Team (IPT) and serve as the data and technology lead, driving integration between scientific, regulatory, and engineering functions to support submission-ready data and regulatory intelligence solutions. Roles & Responsibilities: Functional Skills: Lead the engineering strategy and implementation for end-to-end regulatory operations, including data ingestion, transformation, integration, and delivery across regulatory systems. Serve as the data engineering SME in the Integrated Product Team (IPT) to support regulatory submissions, agency interactions, and lifecycle updates. Collaborate with global regulatory affairs, clinical, CMC, quality, safety, and IT teams to gather submission data requirements and translate them into data engineering solutions. Manage and oversee the development of data pipelines, data models, and metadata frameworks that support submission data standards (e.g., eCTD, IDMP, SPL, xEVMPD). Enable integration and reporting across regulatory information management systems (RIMS), EDMS, clinical trial systems, and lab data platforms. Implement data governance, lineage, validation, and audit trails for regulatory data workflows, ensuring GxP and regulatory compliance. Guide the development of automation solutions, dashboards, and analytics that improve visibility into submission timelines, data quality, and regulatory KPIs. Ensure interoperability between regulatory data platforms and enterprise data lakes or lakehouses for cross-functional reporting and insights. Collaborate with IT, data governance, and enterprise architecture teams to ensure alignment with overall data strategy and compliance frameworks. Drive innovation by evaluating emerging technologies in data engineering, graph data, knowledge management, and AI for regulatory intelligence. Lead, mentor, and coach a small team of data engineers and analysts, fostering a culture of excellence, innovation, and delivery. Drive Agile and Scaled Agile (SAFe) methodologies, managing sprint backlogs, prioritization, and iterative improvements to enhance team velocity and project delivery. Stay up-to-date with emerging data technologies, industry trends, and best practices, ensuring the organization leverages the latest innovations in data engineering and architecture. Must-Have Skills: 812 years of experience in data engineering or data architecture, with 3+ years in a senior or managerial capacity, preferably within the biotech or pharmaceutical industry. Proven experience supporting regulatory functions, including submissions, tracking, and reporting for FDA, EMA, and other global authorities. Experience with ETL/ELT tools, data pipelines, and cloud-based data platforms (e.g., Databricks, AWS, Azure, or GCP). Familiarity with regulatory standards and data models such as eCTD, IDMP, HL7, CDISC, and xEVMPD. Deep understanding of GxP data compliance, audit requirements, and regulatory submission processes. Experience with tools like Power BI, Tableau, or Qlik for regulatory dashboarding and visualization is a plus. Strong project management, stakeholder communication, and leadership skills, especially in matrixed, cross-functional environments. Ability to translate technical capabilities into regulatory and business outcomes.Prepare team members for stakeholder discussions by helping assess data costs, access requirements, dependencies, and availability for business scenarios. Good-to-Have Skills: Prior experience working on integrated product teams or regulatory transformation programs. Knowledge of Regulatory Information Management Systems (RIMS), Veeva Vault RIM, or Master Data Management (MDM) in regulated environments. Familiarity with Agile/SAFe methodologies and DevOps/DataOps best practices. Education and Professional Certifications 12 to 15 years of experience in Computer Science, IT or related field Scaled Agile SAFe certification preferred Project Management certifications preferred Soft Skills: Excellent analytical and troubleshooting skills Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation Ability to manage multiple priorities successfully Team-oriented, with a focus on achieving team goals Strong presentation and public speaking skills

What you will do The R&D Precision Medicine team is responsible for Data Standardization, Data Searching, Cohort Building, and Knowledge Management tools that provide the Amgen scientific community with access to Amgens wealth of human datasets, projects and study histories, and knowledge over various scientific findings. These data include clinical data, omics, and images. These solutions are pivotal tools in Amgens goal to accelerate the speed of discovery, and speed to market of advanced precision medications. The Data Engineer will be responsible for full stack development of enterprise analytics and data mastering solutions leveraging Databricks and Power BI. This role requires expertise in both data architecture and analytics, with the ability to create scalable, reliable, and high-performing enterprise solutions that support research cohort-building and advanced AI pipelines. The ideal candidate will have experience creating and surfacing large unified repositories of human data, based on integrations from multiple repositories and solutions, and be exceptionally skilled with data analysis and profiling. You will collaborate closely with partners, product team members, and related IT teams, to design and implement data models, integrate data from various sources, and ensure best practices for data governance and security. The ideal candidate will have a solid background in data warehousing, ETL, Databricks, Power BI, and enterprise data mastering. Roles & Responsibilities Design and build scalable enterprise analytics solutions using Databricks, Power BI, and other modern data management tools. Leverage data virtualization, ETL, and semantic layers to balance need for unification, performance, and data transformation with goal to reduce data proliferation Break down features into work that aligns with the architectural direction runway Participate hands-on in pilots and proofs-of-concept for new patterns Create robust documentation from data analysis and profiling, and proposed designs and data logic Develop advanced sql queries to profile, and unify data Develop data processing code in sql, along with semantic views to prepare data for reporting Develop PowerBI Models and reporting packages Design robust data models, and processing layers, that support both analytical processing and operational reporting needs. Design and develop solutions based on best practices for data governance, security, and compliance within Databricks and Power BI environments. Ensure the integration of data systems with other enterprise applications, creating seamless data flows across platforms. Develop and maintain Power BI solutions, ensuring data models and reports are optimized for performance and scalability. Collaborate with partners to define data requirements, functional specifications, and project goals. Continuously evaluate and adopt new technologies and methodologies to enhance the architecture and performance of data solutions. What we expect of you We are all different, yet we all use our unique contributions to serve patients. The professional we seek is someone with these qualifications. Basic Qualifications: Masters degree with 1 to 3 years of experience in Data Engineering OR Bachelors degree with 1 to 3 years of experience in Data Engineering Must-Have Skills: Minimum of 1 year of hands-on experience with BI solutions (Preferrable Power BI or Business Objects) including report development, dashboard creation, and optimization. Minimum of 1 year of hands-on experience building Change-data-capture (CDC) ETL pipelines, data warehouse design and build, and enterprise-level data management. Hands-on experience with Databricks, including data engineering, optimization, and analytics workloads. Experience using cloud platforms (AWS), data lakes, and data warehouses. Working knowledge of ETL processes, data pipelines, and integration technologies. Good communication and collaboration skills to work with cross-functional teams and senior leadership. Ability to assess business needs and design solutions that align with organizational goals. Exceptional hands-on capabilities with data profiling and data anlysis Good-to-Have Skills: Experience with human data, ideally human healthcare data Familiarity with laboratory testing, patient data from clinical care, HL7, FHIR, and/or clinical trial data management Professional Certifications: ITIL Foundation or other relevant certifications (preferred) SAFe Agile Practitioner (6.0) Microsoft CertifiedData Analyst Associate (Power BI) or related certification. Databricks Certified Professional or similar certification. Soft Skills: Excellent analytical and troubleshooting skills Deep intellectual curiosity Highest degree of initiative and self-motivation Strong verbal and written communication skills, including presentation to varied audiences of complex technical/business topics Confidence technical leader Ability to work effectively with global, virtual teams, specifically including using of tools and artifacts to assure clear and efficient collaboration across time zones Ability to manage multiple priorities successfully Team-oriented, with a focus on achieving team goals Strong problem solving, analytical skills; Ability to learn quickly and retain and synthesize complex information from diverse sources

Role Description: We are looking for highly motivated expert Senior Data Engineer who can own the design & development of complex data pipelines, solutions and frameworks. The ideal candidate will be responsible to design, develop, and optimize data pipelines, data integration frameworks, and metadata-driven architectures that enable seamless data access and analytics. This role prefers deep expertise in big data processing, distributed computing, data modeling, and governance frameworks to support self-service analytics, AI-driven insights, and enterprise-wide data management. Roles & Responsibilities: Design, develop, and maintain scalable ETL/ELT pipelines to support structured, semi-structured, and unstructured data processing across the Enterprise Data Fabric. Implement real-time and batch data processing solutions, integrating data from multiple sources into a unified, governed data fabric architecture. Optimize big data processing frameworks using Apache Spark, Hadoop, or similar distributed computing technologies to ensure high availability and cost efficiency. Work with metadata management and data lineage tracking tools to enable enterprise-wide data discovery and governance. Ensure data security, compliance, and role-based access control (RBAC) across data environments. Optimize query performance, indexing strategies, partitioning, and caching for large-scale data sets. Develop CI/CD pipelines for automated data pipeline deployments, version control, and monitoring. Implement data virtualization techniques to provide seamless access to data across multiple storage systems. Collaborate with cross-functional teams, including data architects, business analysts, and DevOps teams, to align data engineering strategies with enterprise goals. Stay up to date with emerging data technologies and best practices, ensuring continuous improvement of Enterprise Data Fabric architectures. Must-Have Skills: Hands-on experience in data engineering technologies such as Databricks, PySpark, SparkSQL Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies. Proficiency in workflow orchestration, performance tuning on big data processing. Strong understanding of AWS services Experience with Data Fabric, Data Mesh, or similar enterprise-wide data architectures. Ability to quickly learn, adapt and apply new technologies Strong problem-solving and analytical skills Excellent communication and teamwork skills Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices. Good-to-Have Skills: Good to have deep expertise in Biotech & Pharma industries Experience in writing APIs to make the data available to the consumers Experienced with SQL/NOSQL database, vector database for large language models Experienced with data modeling and performance tuning for both OLAP and OLTP databases Experienced with software engineering best-practices, including but not limited to version control (Git, Subversion, etc.), CI/CD (Jenkins, Maven etc.), automated unit testing, and Dev Ops Education and Professional Certifications 9 to 12 years of Computer Science, IT or related field experience AWS Certified Data Engineer preferred Databricks Certificate preferred Scaled Agile SAFe certification preferred Soft Skills: Excellent analytical and troubleshooting skills. Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented, with a focus on achieving team goals. Ability to learn quickly, be organized and detail oriented. Strong presentation and public speaking skills.

Role Description: As part of the cybersecurity organization, In this vital role you will be responsible for designing, building, and maintaining data infrastructure to support data-driven decision-making. This role involves working with large datasets, developing reports, executing data governance initiatives, and ensuring data is accessible, reliable, and efficiently managed. The role sits at the intersection of data infrastructure and business insight delivery, requiring the Data Engineer to design and build robust data pipelines while also translating data into meaningful visualizations for stakeholders across the organization. The ideal candidate has strong technical skills, experience with big data technologies, and a deep understanding of data architecture, ETL processes, and cybersecurity data frameworks. Roles & Responsibilities: Design, develop, and maintain data solutions for data generation, collection, and processing. Be a key team member that assists in design and development of the data pipeline. Build data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems. Develop and maintain interactive dashboards and reports using tools like Tableau, ensuring data accuracy and usability Schedule and manage workflows the ensure pipelines run on schedule and are monitored for failures. Collaborate with multi-functional teams to understand data requirements and design solutions that meet business needs. Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency. Implement data security and privacy measures to protect sensitive data. Leverage cloud platforms (AWS preferred) to build scalable and efficient data solutions. Collaborate and communicate effectively with product teams. Collaborate with data scientists to develop pipelines that meet dynamic business needs. Share and discuss findings with team members practicing SAFe Agile delivery model. Basic Qualifications: Masters degree and 1 to 3 years of experience of Computer Science, IT or related field experience OR Bachelors degree and 3 to 5 years of Computer Science, IT or related field experience OR Diploma and 7 to 9 years of Computer Science, IT or related field experience Preferred Qualifications: Hands on experience with data practices, technologies, and platforms, such as Databricks, Python, GitLab, LucidChart, etc. Hands-on experience with data visualization and dashboarding toolsTableau, Power BI, or similar is a plus Proficiency in data analysis tools (e.g. SQL) and experience with data sourcing tools Excellent problem-solving skills and the ability to work with large, complex datasets Understanding of data governance frameworks, tools, and best practices Knowledge of and experience with data standards (FAIR) and protection regulations and compliance requirements (e.g., GDPR, CCPA) Good-to-Have Skills: Experience with ETL tools and various Python packages related to data processing, machine learning model development Strong understanding of data modeling, data warehousing, and data integration concepts Knowledge of Python/R, Databricks, cloud data platforms Experience working in Product team's environment Experience working in an Agile environment Professional Certifications: AWS Certified Data Engineer preferred Databricks Certificate preferred Soft Skills: Initiative to explore alternate technology and approaches to solving problems Skilled in breaking down problems, documenting problem statements, and estimating efforts Excellent analytical and troubleshooting skills Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation Ability to handle multiple priorities successfully Team-oriented, with a focus on achieving team goals

Hiring: Data Engineering Senior Software Engineer / Tech Lead / Senior Tech Lead - Hybrid (3 Days from office) | Shift: 2 PM 11 PM IST - Experience: 5 to 12+ years (based on role & grade) Open Grades/Roles : Senior Software Engineer : 58 Years Tech Lead : 7–10 Years Senior Tech Lead : 10–12+ Years Job Description – Data Engineering Team Core Responsibilities (Common to All Levels) : Design, build and optimize ETL/ELT pipelines using tools like Pentaho , Talend , or similar Work on traditional databases (PostgreSQL, MSSQL, Oracle) and MPP/modern systems (Vertica, Redshift, BigQuery, MongoDB) Collaborate cross-functionally with BI, Finance, Sales, and Marketing teams to define data needs Participate in data modeling (ER/DW/Star schema) , data quality checks , and data integration Implement solutions involving messaging systems (Kafka) , REST APIs , and scheduler tools (Airflow, Autosys, Control-M) Ensure code versioning and documentation standards are followed (Git/Bitbucket) Additional Responsibilities by Grade Senior Software Engineer (5–8 Yrs) : Focus on hands-on development of ETL pipelines, data models, and data inventory Assist in architecture discussions and POCs Good to have: Tableau/Cognos, Python/Perl scripting, GCP exposure Tech Lead (7–10 Yrs) : Lead mid-sized data projects and small teams Decide on ETL strategy (Push Down/Push Up) and performance tuning Strong working knowledge of orchestration tools, resource management, and agile delivery Senior Tech Lead (10–12+ Yrs) : Drive data architecture , infrastructure decisions , and internal framework enhancements Oversee large-scale data ingestion, profiling, and reconciliation across systems Mentoring junior leads and owning stakeholder delivery end-to-end Advantageous: Experience with AdTech/Marketing data , Hadoop ecosystem (Hive, Spark, Sqoop) - Must-Have Skills (All Levels): ETL Tools: Pentaho / Talend / SSIS / Informatica Databases: PostgreSQL, Oracle, MSSQL, Vertica / Redshift / BigQuery Orchestration: Airflow / Autosys / Control-M / JAMS Modeling: Dimensional Modeling, ER Diagrams Scripting: Python or Perl (Preferred) Agile Environment, Git-based Version Control Strong Communication and Documentation

Seniority : Senior Description & Requirements Position Summary The Senior AI Engineer with GenAI expertise is responsible for developing advanced technical solutions, integrating cutting-edge generative AI technologies. This role requires a deep understanding of modern technical and cloud-native practices, AI, DevOps, and machine learning technologies, particularly in generative models. You will support a wide range of customers through the Ideation to MVP journey, showcasing leadership and decision-making abilities while tackling complex challenges. Key Responsibilities Technical & Engineering Leadership Develop solutions leveraging GenAI technologies, integrating advanced AI capabilities into cloud-native architectures to enhance system functionality and scalability. Lead the design and implementation of GenAI-driven applications, ensuring seamless integration with microservices and container-based environments. Create solutions that fully leverage the capabilities of modern microservice and container-based environments running in public, private, and hybrid clouds. Contribute to HCL thought leadership across the Cloud Native domain with an expert understanding of open-source technologies (e.g., Kubernetes/CNCF) and partner technologies. Collaborate on joint technical projects with partners, including Google, Microsoft, AWS, IBM, Red Hat, Intel, Cisco, and Dell/VMware. Service Delivery Engineer innovative GenAI solutions from ideation to MVP, ensuring high performance and reliability within cloud-native frameworks. Optimize AI models for deployment in cloud environments, balancing efficiency and effectiveness to meet client requirements and industry standards. Assess existing complex solutions and recommend appropriate technical treatments to transform applications with cloud-native/12-factor characteristics. Refactor existing solutions to implement a microservices-based architecture. Innovation & Initiative Drive the adoption of cutting-edge GenAI technologies within cloud-native projects, spearheading initiatives that push the boundaries of AI integration in cloud services. Engage in technical innovation and support HCLs position as an industry leader. Author whitepapers, blogs, and speak at industry events. Maintain hands-on technical credibility, stay ahead of industry trends, and mentor others. Client Relationships Provide expert guidance to clients on incorporating GenAI and machine learning into their cloud-native systems, ensuring best practices and strategic alignment with business goals. Conduct workshops and briefings to educate clients on the benefits and applications of GenAI, establishing strong, trust-based relationships. Perform a trusted advisor role, contributing to technical projects (PoCs and MVPs) with a strong focus on technical excellence and on-time delivery. Mandatory Skills & Experience A passionate developer with 10+ years of experience in Java, Python, Node.js, and Spring programming, comfortable working as part of a paired/balanced team. Extensive experience in software development, with significant exposure to AI/ML technologies. Expertise in GenAI frameworks: Proficient in using GenAI frameworks and libraries such as LangChain, OpenAI API, Gemini, and Hugging Face Transformers. Prompt engineering: Experience in designing and optimizing prompts for various AI models to achieve desired outputs and improve model performance. Strong understanding of NLP techniques and tools, including tokenization, embeddings, transformers, and language models. Proven experience developing complex solutions that leverage cloud-native technologiesfeaturing container-based, microservices-based approaches; based on applying 12-factor principles to application engineering. Exemplary verbal and written communication skills (English). Positive and solution-oriented mindset. Solid experience delivering Agile and Scrum projects in a Jira-based project management environment. Proven leadership skills and the ability to inspire and manage teams. Desired Skills & Experience Machine Learning Operations (MLOps): Experience in deploying, monitoring, and maintaining AI models in production environments using MLOps practices. Data engineering for AI: Skilled in data preprocessing, feature engineering, and creating pipelines to feed AI models with high-quality data. AI model fine-tuning: Proficiency in fine-tuning pre-trained models on specific datasets to improve performance for specialized tasks. AI ethics and bias mitigation: Knowledgeable about ethical considerations in AI and experienced in implementing strategies to mitigate bias in AI models. Knowledgeable about vector databases, LLMs, and SMLs, and integrating with such models. Proficient with Kubernetes and other cloud-native technologies, including experience with commercial Kubernetes distributions (e.g., Red Hat OpenShift, VMware Tanzu, Google Anthos, Azure AKS, Amazon EKS, Google GKE). Deep understanding of core practices including DevOps, SRE, Agile, Scrum, XP, Domain-Driven Design, and familiarity with the CNCF open-source community. Recognized with multiple cloud and technical certifications at a professional level, ideally including AI/ML specializations from providers like Google, Microsoft, AWS, Linux Foundation, IBM, or Red Hat. Verifiable Certification At least one recognized cloud professional / developer certification (AWS/Google/Microsoft)

Hands-on Data Engineer with strong Databricks expertise in Git/DevOps integration, Unity Catalog governance, and performance tuning of data transformation workloads. Skilled in optimizing pipelines and ensuring secure, efficient data operations.

About the Company Greetings from Teamware Solutions a division of Quantum Leap Consulting Pvt. Ltd About the Role We are hiring a Data Architecture Location: Bangalore Work Model: Hybrid Experience: 5-9 Years Notice Period: Immediate to 15 Days Job Description: Data Architecture, Data Governance, Data Modeling Additional Information: Mandatory Skills: Data Architecture, Data Governance, Data Modeling Nice to have skills Certification in Data Engineering Interview Mode Virtual Interview minimum 5 yrs relevant experience and maximum 9 yrs for this requirement. Someone with more experience in building PySpark data streaming jobs on Azure Databricks who have done real projects, have expertise, and hands-on experience also Also, Data governance and data modeling experience with a minimum of 4 years is mandatory Communication should be excellent Please let me know if you are interested in this position and send me your resumes to netra.s@twsol.com

ABOUT THE ROLE Role Description: The Data Scientist is responsible for developing and implementing AI-driven solutions to enhance cybersecurity measures within the organization. This role involves leveraging data science techniques to analyze security data, detect threats, and automate security processes. The Data Scientist will work closely with cybersecurity teams to identify data-driven automation opportunities, strengthening the organizations security posture . Roles & Responsibilities: Develop analytics to address security concerns, enhancements, and capabilities to improve the organization's security posture. Collaborate with Data Engineers to translate security-focused algorithms into effective solutions. Work in technical teams in development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics. Perform exploratory and targeted data analyses using descriptive statistics and other methods to identify security patterns and anomalies. Design and implement security-focused analytics pipelines leveraging MLOps practices. Collaborate with data engineers on data quality assessment, data cleansing, and the development of security-related data pipelines. Contribute to data engineering efforts to refine data infrastructure and ensure scalable, efficient security analytics. Generate reports, annotated code, and other projects artifacts to document, archive, and communicate your work and outcomes. Share and discuss findings with team members practicing SAFe Agile delivery model. Functional Skills: Basic Qualifications: Masters degree and 1 to 3 years of experience with one or more analytic software tools or languages (e.g., SAS, SPSS, R, Python) OR Bachelors degree and 3 to 5 years of experience with one or more analytic software tools or languages (e.g., SAS, SPSS, R, Python) OR Diploma and 7 to 9 years of experience with one or more analytic software tools or languages (e.g., SAS, SPSS, R, Python) Preferred Qualifications: Experience with one or more analytic software tools or languages (e.g., SAS, SPSS, R, Python) Demonstrated skill in the use of applied analytics, descriptive statistics, feature extraction and predictive analytics on industrial datasets Strong foundation in machine learning algorithms and techniques Experience in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification Good-to-Have Skills: Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn) Outstanding analytical and problem-solving skills; Ability to learn quickly; Excellent communication and interpersonal skills Experience with data engineering and pipeline development Experience in analyzing time-series data for forecasting and trend analysis Experience with AWS, Azure, or Google Cloud Experience with Databricks platform for data analytics and MLOps Experience with Generative AI models (e.g., GPT, DALLE, Stable Diffusion) and their applications in cybersecurity and data analysis Experience working in Product team's environment Experience working in an Agile environment Professional Certifications: Any AWS Developer certification (preferred) Any Python and ML certification (preferred) Any SAFe Agile certification (preferred) Soft Skills: Initiative to explore alternate technology and approaches to solving problems Skilled in breaking down problems, documenting problem statements, and estimating efforts Excellent analytical and troubleshooting skills Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation Ability to manage multiple priorities successfully Team-oriented, with a focus on achieving team goals.

What you will do In this vital role We are seeking a Associate Data Engineer to design, build, and maintain scalable data solutions that drive business insights. You will work with large datasets, cloud platforms (AWS preferred), and big data technologies to develop ETL pipelines, ensure data quality, and support data governance initiatives. Develop and maintain data pipelines, ETL/ELT processes, and data integration solutions . Design and implement data models, data dictionaries, and documentation for accuracy and consistency. Ensure data security, privacy, and governance standard processes. Use Databricks, Apache Spark (PySpark, SparkSQL), AWS, Redshift, for scalable data processing. Collaborate with cross-functional teams to understand data needs and deliver actionable insights. Optimize data pipeline performance and explore new tools for efficiency. Follow best practices in coding, testing, and infrastructure-as-code (CI/CD, version control, automated testing) . What we expect of you We are all different, yet we all use our unique contributions to serve patients. Strong problem-solving, critical thinking, and communication skills. Ability to collaborate effectively in a team setting. Proficiency in SQL, data analysis tools, and data visualization . Hands-on experience with big data technologies (Databricks, Apache Spark, AWS, Redshift ) . Experience with ETL tools, workflow orchestration, and performance tuning for big data . Basic Qualifications: Bachelors degree and 0 to 3 years of experience OR Diploma and 4 to 7 years of experience in Computer science, IT or related field. Preferred Qualifications: Knowledge of data modeling, warehousing, and graph databases Experience with Python, SageMaker, and cloud data platforms . AWS Certified Data Engineer or Databricks certification preferred.

What you will do In this vital role you will create and develop data lake solutions for scientific data that drive business decisions for Research. You will build scalable and high-performance data engineering solutions for large scientific datasets and collaborate with Research collaborators. You will also provide technical leadership to junior team members. The ideal candidate possesses experience in the pharmaceutical or biotech industry, demonstrates deep technical skills, is proficient with big data technologies, and has a deep understanding of data architecture and ETL processes. Roles & Responsibilities: Lead, manage, and mentor a high-performing team of data engineers Design, develop, and implement data pipelines, ETL processes, and data integration solutions Take ownership of data pipeline projects from inception to deployment, manage scope, timelines, and risks Develop and maintain data models for biopharma scientific data, data dictionaries, and other documentation to ensure data accuracy and consistency Optimize large datasets for query performance Collaborate with global multi-functional teams including research scientists to understand data requirements and design solutions that meet business needs Implement data security and privacy measures to protect sensitive data Leverage cloud platforms (AWS preferred) to build scalable and efficient data solutions Collaborate with Data Architects, Business SMEs, Software Engineers and Data Scientists to design and develop end-to-end data pipelines to meet fast paced business needs across geographic regions Identify and resolve data-related challenges Adhere to best practices for coding, testing, and designing reusable code/component Explore new tools and technologies that will help to improve ETL platform performance Participate in sprint planning meetings and provide estimations on technical implementation What we expect of you We are all different, yet we all use our unique contributions to serve patients. The [vital attribute] professional we seek is a [type of person] with these qualifications. Basic Qualifications: Doctorate Degree OR Masters degree with 4 - 6 years of experience in Computer Science, IT, Computational Chemistry, Computational Biology/Bioinformatics or related field OR Bachelors degree with 6 - 8 years of experience in Computer Science, IT, Computational Chemistry, Computational Biology/Bioinformatics or related field OR Diploma with 10 - 12 years of experience in Computer Science, IT, Computational Chemistry, Computational Biology/Bioinformatics or related field Preferred Qualifications: 3+ years of experience in implementing and supporting biopharma scientific research data analytics (software platforms) Functional Skills: Must-Have Skills: Proficiency in SQL and Python for data engineering, test automation frameworks (pytest), and scripting tasks Hands on experience with big data technologies and platforms, such as Databricks, Apache Spark (PySpark, SparkSQL), workflow orchestration, performance tuning on big data processing Excellent problem-solving skills and the ability to work with large, complex datasets Able to engage with business collaborators and mentor team to develop data pipelines and data models Good-to-Have Skills: A passion for tackling complex challenges in drug discovery with technology and data Good understanding of data modeling, data warehousing, and data integration concepts Good experience using RDBMS (e.g. Oracle, MySQL, SQL server, PostgreSQL) Knowledge of cloud data platforms (AWS preferred) Experience with data visualization tools (e.g. Dash, Plotly, Spotfire) Experience with diagramming and collaboration tools such as Miro, Lucidchart or similar tools for process mapping and brainstorming Experience writing and maintaining technical documentation in Confluence Understanding of data governance frameworks, tools, and best practices Professional Certifications: Databricks Certified Data Engineer Professional preferred Soft Skills: Excellent critical-thinking and problem-solving skills Good communication and collaboration skills Demonstrated awareness of how to function in a team setting Demonstrated presentation skills

What you will do In this vital role, you will be responsible for the end-to-end development of an enterprise analytics and data mastering solution using Databricks and Power BI. This role requires expertise in both data architecture and analytics, with the ability to create scalable, reliable, and impactful enterprise solutions that research cohort-building and advanced research pipeline. The ideal candidate will have experience creating and surfacing large unified repositories of human data, based on integrations from multiple repositories and solutions, and be extraordinarily skilled with data analysis and profiling. You will collaborate closely with key customers, product team members, and related IT teams, to design and implement data models, integrate data from various sources, and ensure best practices for data governance and security. The ideal candidate will have a good background in data warehousing, ETL, Databricks, Power BI, and enterprise data mastering. Design and build scalable enterprise analytics solutions using Databricks, Power BI, and other modern data tools. Leverage data virtualization, ETL, and semantic layers to balance need for unification, performance, and data transformation with goal to reduce data proliferation Break down features into work that aligns with the architectural direction runway Participate hands-on in pilots and proofs-of-concept for new patterns Create robust documentation from data analysis and profiling, and proposed designs and data logic Develop advanced sql queries to profile, and unify data Develop data processing code in sql, along with semantic views to prepare data for reporting Develop PowerBI Models and reporting packages Design robust data models, and processing layers, that support both analytical processing and operational reporting needs. Design and develop solutions based on best practices for data governance, security, and compliance within Databricks and Power BI environments. Ensure the integration of data systems with other enterprise applications, creating seamless data flows across platforms. Develop and maintain Power BI solutions, ensuring data models and reports are optimized for performance and scalability. Collaborate with key customers to define data requirements, functional specifications, and project goals. Continuously evaluate and adopt new technologies and methodologies to enhance the architecture and performance of data solutions. What we expect of you We are all different, yet we all use our unique contributions to serve patients. The R&D Data Catalyst Team is responsible for building Data Searching, Cohort Building, and Knowledge Management tools that provide the Amgen scientific community with visibility to Amgens wealth of human datasets, projects and study histories, and knowledge over various scientific findings. These solutions are pivotal tools in Amgens goal to accelerate the speed of discovery, and speed to market of advanced precision medications. Basic Qualifications: Masters degree and 1 to 3 years of Data Engineering experience OR Bachelors degree and 3 to 5 years of Data Engineering experience OR Diploma and 7 to 9 years of Data Engineering experience Must Have Skills: Minimum of 3 years of hands-on experience with BI solutions (Preferable Power BI or Business Objects) including report development, dashboard creation, and optimization. Minimum of 3 years of hands-on experience building Change-data-capture (CDC) ETL pipelines, data warehouse design and build, and enterprise-level data management. Hands-on experience with Databricks, including data engineering, optimization, and analytics workloads. Deep understanding of Power BI, including model design, DAX, and Power Query. Proven experience designing and implementing data mastering solutions and data governance frameworks. Expertise in cloud platforms (AWS), data lakes, and data warehouses. Strong knowledge of ETL processes, data pipelines, and integration technologies. Good communication and collaboration skills to work with cross-functional teams and senior leadership. Ability to assess business needs and design solutions that align with organizational goals. Exceptional hands-on capabilities with data profiling, data transformation, data mastering Success in mentoring and training team members Good to Have Skills: ITIL Foundation or other relevant certifications (preferred) SAFe Agile Practitioner (6.0) Microsoft Certified: Data Analyst Associate (Power BI) or related certification. Databricks Certified Professional or similar certification. Soft Skills: Excellent analytical and troubleshooting skills Deep intellectual curiosity The highest degree of initiative and self-motivation Strong verbal and written communication skills, including presentation to varied audiences of complex technical/business topics Confidence technical leader Ability to work effectively with global, remote teams, specifically including using of tools and artifacts to assure clear and efficient collaboration across time zones Ability to handle multiple priorities successfully Team-oriented, with a focus on achieving team goals Strong problem solving, analytical skills; Ability to learn quickly and retain and synthesize complex information from diverse sources.

ABOUT THE ROLE Role Description: We are seeking a seasoned Senior Engineering Manager ( Data Engineer ing) to lead the end-to-end management of enterprise data assets and operational data workflows. This role is critical in ensuring the availability, quality, consistency, and timeliness of data across platforms and functions, supporting analytics, reporting, compliance, and digital transformation initiatives. As a senior leader in the data organization, you will oversee the day-to-day data operations, manage a team of data professionals, and drive process excellence in data intake, transformation, validation, and delivery. You will work closely with cross-functional teams including data engineering, analytics, IT, governance, and business stakeholders to align operational data capabilities with enterprise needs. Roles & Responsibilities: Lead and manage the enterprise data operations team, responsible for data ingestion, processing, validation, quality control, and publishing to various downstream systems. Define and implement standard operating procedures for data lifecycle management, ensuring availability, accuracy, completeness, and integrity of critical data assets. Oversee and continuously improve daily operational workflows, including scheduling, monitoring, and troubleshooting data jobs across cloud and on-premise environments. Establish and track key data operations metrics (SLAs, throughput, latency, data quality, incident resolution) and drive continuous improvements. Partner with data engineering and platform teams to optimize pipelines, support new data integrations, and ensure scalability and resilience of operational data flows. Collaborate with data governance, compliance, and security teams to maintain regulatory compliance, data privacy, and access controls. Serve as the primary escalation point for data incidents and outages, ensuring rapid response and root cause analysis. Build strong relationships with business and analytics teams to understand data consumption patterns, prioritize operational needs, and align with business objectives. Drive adoption of best practices for documentation, metadata, lineage, and change management across data operations processes. Mentor and develop a high-performing team of data operations analysts and leads. Functional Skills: Must-Have Skills: Experience managing a team of data engineers in biotech/pharmadomain companies. Experience in designing and maintainingdata pipelines and analytics solutions that extract, transform, and load data from multiple source systems. Demonstrated hands-on experience with cloud platforms (AWS) and the ability to architect cost-effective and scalable data solutions. Experience managing data workflows on Databricks in cloud environments such as AWS, Azure, or GCP. Strong problem-solving skills with the ability to analyze complex data flow issues and implement sustainable solutions. Working knowledge of SQL, Python, PySparkor scripting languages for process monitoring and automation. Experience collaborating with data engineering, analytics, IT operations, and business teams in a matrixed organization. Familiarity with data governance, metadata management, access control, and regulatory requirements (e.g., GDPR, HIPAA, SOX). Excellent leadership, communication, and stakeholder engagement skills. Well versed with full stack development& DataOps automation, logging & observability frameworks, and pipeline orchestration tools. Strong analytical and problem-solving skills to address complex data challenges. Effective communication and interpersonal skills to collaborate with cross-functional teams. Good-to-Have Skills: Data Engineering Management experience in Biotech/Life Sciences/Pharma Experience using graph databases such as Stardog or Marklogic or Neo4J or Allegrograph, etc. Education and Professional Certifications 12 to 15 years of experience in Computer Science, IT or related field Databricks Certificate preferred Scaled Agile SAFe certification preferred Experience in life sciences, healthcare, or other regulated industries with large-scale operational data environments. Familiarity with incident and change management processes (e.g., ITIL). Soft Skills: Excellent analytical and troubleshooting skills Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation Ability to manage multiple priorities successfully Team-oriented, with a focus on achieving team goals Strong presentation and public speaking skills.

Role Description: We are looking for highly motivated expert Senior Data Engineer who can own the design & development of complex data pipelines, solutions and frameworks. The ideal candidate will be responsible to design, develop, and optimize data pipelines, data integration frameworks, and metadata-driven architectures that enable seamless data access and analytics. This role prefers deep expertise in big data processing, distributed computing, data modeling, and governance frameworks to support self-service analytics, AI-driven insights, and enterprise-wide data management. Roles & Responsibilities: Design, develop, and maintain scalable ETL/ELT pipelines to support structured, semi-structured, and unstructured data processing across the Enterprise Data Fabric. Implement real-time and batch data processing solutions, integrating data from multiple sources into a unified, governed data fabric architecture. Optimize big data processing frameworks using Apache Spark, Hadoop, or similar distributed computing technologies to ensure high availability and cost efficiency. Work with metadata management and data lineage tracking tools to enable enterprise-wide data discovery and governance. Ensure data security, compliance, and role-based access control (RBAC) across data environments. Optimize query performance, indexing strategies, partitioning, and caching for large-scale data sets. Develop CI/CD pipelines for automated data pipeline deployments, version control, and monitoring. Implement data virtualization techniques to provide seamless access to data across multiple storage systems. Collaborate with cross-functional teams, including data architects, business analysts, and DevOps teams, to align data engineering strategies with enterprise goals. Stay up to date with emerging data technologies and best practices, ensuring continuous improvement of Enterprise Data Fabric architectures. Must-Have Skills: Hands-on experience in data engineering technologies such as Databricks, PySpark, SparkSQL Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies. Proficiency in workflow orchestration, performance tuning on big data processing. Strong understanding of AWS services Experience with Data Fabric, Data Mesh, or similar enterprise-wide data architectures. Ability to quickly learn, adapt and apply new technologies Strong problem-solving and analytical skills Excellent communication and teamwork skills Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices. Good-to-Have Skills: Good to have deep expertise in Biotech & Pharma industries Experience in writing APIs to make the data available to the consumers Experienced with SQL/NOSQL database, vector database for large language models Experienced with data modeling and performance tuning for both OLAP and OLTP databases Experienced with software engineering best-practices, including but not limited to version control (Git, Subversion, etc.), CI/CD (Jenkins, Maven etc.), automated unit testing, and DevOps Education and Professional Certifications Masters degree and 3 to 4 + years of Computer Science, IT or related field experience OR Bachelors degree and 5 to 8 + years of Computer Science, IT or related field experience AWS Certified Data Engineer preferred Databricks Certificate preferred Scaled Agile SAFe certification preferred Soft Skills: Excellent analytical and troubleshooting skills. Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented, with a focus on achieving team goals. Ability to learn quickly, be organized and detail oriented. Strong presentation and public speaking skills.

The R&D Data Catalyst Team is responsible for building Data Searching, Cohort Building, and Knowledge Management tools that provide the Amgen scientific community with visibility to Amgens wealth of human datasets, projects and study histories, and knowledge over various scientific findings . These solutions are pivotal tools in Amgens goal to accelerate the speed of discovery, and speed to market of advanced precision medications . The Data Engineer will be responsible for the end-to-end development of an enterprise analytics and data mastering solution leveraging Databricks and Power BI. This role requires expertise in both data architecture and analytics, with the ability to create scalable, reliable, and high-performing enterprise solutions that research cohort-building and advanced research pipeline . The ideal candidate will have experience creating and surfacing large unified repositories of human data, based on integrations from multiple repositories and solutions , and be exceptionally skilled with data analysis and profiling . You will collaborate closely with stakeholders , product team members , and related I T teams, to design and implement data models, integrate data from various sources, and ensure best practices for data governance and security. The ideal candidate will have a strong background in data warehousing, ETL, Databricks, Power BI, and enterprise data mastering. Roles & Responsibilities: Design and build scalable enterprise analytics solutions using Databricks, Power BI, and other modern data tools. Leverage data virtualization, ETL, and semantic layers to balance need for unification, performance, and data transformation with goal to reduce data proliferation Break down features into work that aligns with the architectural direction runway Participate hands-on in pilots and proofs-of-concept for new patterns Create robust documentation from data analysis and profiling, and proposed designs and data logic Develop advanced sql queries to profile, and unify data Develop data processing code in sql, along with semantic views to prepare data for reporting Develop PowerBI Models and reporting packages Design robust data models, and processing layers, that support both analytical processing and operational reporting needs. Design and develop solutions based on best practices for data governance, security, and compliance within Databricks and Power BI environments. Ensure the integration of data systems with other enterprise applications, creating seamless data flows across platforms. Develop and maintain Power BI solutions, ensuring data models and reports are optimized for performance and scalability. Collaborate with stakeholders to define data requirements, functional specifications, and project goals. Continuously evaluate and adopt new technologies and methodologies to enhance the architecture and performance of data solutions. Basic Qualifications and Experience: Masters degree with 1 to 3years of experience in Data Engineering OR Bachelors degree with 4 to 5 years of experience in Data Engineering Diploma and 7 to 9 years of experience in Data Engineering. Functional Skills: Must-Have Skills : Minimum of 3 years of hands-on experience with BI solutions (Preferrable Power BI or Business Objects) including report development, dashboard creation, and optimization. Minimum of 3years of hands-on experience building Change-data-capture (CDC) ETL pipelines, data warehouse design and build, and enterprise-level data management. Hands-on experience with Databricks, including data engineering, optimization, and analytics workloads. Deep understanding of Power BI, including model design, DAX, and Power Query. Proven experience designing and implementing data mastering solutions and data governance frameworks. Expertise in cloud platforms (AWS), data lakes, and data warehouses. Strong knowledge of ETL processes, data pipelines, and integration technologies. Strong communication and collaboration skills to work with cross-functional teams and senior leadership. Ability to assess business needs and design solutions that align with organizational goals. Exceptional hands-on capabilities with data profiling, data transformation, data mastering Success in mentoring and training team members Good-to-Have Skills: Experience in developing differentiated and deliverable solutions Experience with human data, ideally human healthcare data Familiarity with laboratory testing, patient data from clinical care, HL7, FHIR, and/or clinical trial data management Professional Certifications (please mention if the certification is preferred or mandatory for the role): ITIL Foundation or other relevant certifications (preferred) SAFe Agile Practitioner (6.0) Microsoft Certified: Data Analyst Associate (Power BI) or related certification. Databricks Certified Professional or similar certification. Soft Skills: Excellent analytical and troubleshooting skills Deep intellectual curiosity Highest degree of initiative and self-motivation Strong verbal and written communication skills, including presentation to varied audiences of complex technical/business topics Confidence technical leader Ability to work effectively with global, virtual teams, specifically including leveraging of tools and artifacts to assure clear and efficient collaboration across time zones Ability to manage multiple priorities successfully Team-oriented, with a focus on achieving team goals Strong problem solving, analytical skills; Ability to learn quickly and retain and synthesize complex information from diverse sources