7 Logging Framework Jobs

ABOUT THE ROLE Role Description: We are seeking a seasoned Engineering Manager (Data Engineering) 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. You will be responsible for 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 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/pharma domain companies. Experience in designing and maintaining data 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 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, or 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 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 Doctorate Degree with 3-5 + years of experience in Computer Science, IT or related field OR Masters degree with 6 - 8 + years of experience in Computer Science, IT or related field OR Bachelors degree with 10 - 12 + years of experience in Computer Science, IT or related field 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 Strong presentation and public speaking skills

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. Thorough Embedded C programming knowledge & experience (especially pointers, structures, linked lists etc.,) & Assembly programming knowledge Hands On Experience for Device Driver development with any of standard protocols such as SPI, UART, USB etc., Thorough RTOS knowledge and experience (Mutex, spinlocks, Queues, Signaling, Events, Deferred function calls & Callbacks, Multi-thread & Multi-process environments, Concurrency Scenarios etc.,), Linux Knowledge, Kernel & User Space knowledge Thorough experience of Operating systems, Microprocessor / computer architecture. Strong analytical and debugging skills Hardware and architectural knowledge / experience (Processor Architecture, Cache, interrupts, Memory barriers, Strong ordering etc.,) Emulator, simulator environment & JTAG debugging knowledge / experience Nice to Have - Hands On Experience or knowledge for Inter Processor Communication Protocol, Debug Logging framework, Heap Management & Timer implementations

You will lead the end-to-end management of enterprise data assets and operational data workflows. This critical role ensures availability, quality, consistency, and timeliness of data across platforms and functions, supporting analytics, reporting, compliance, and digital transformation initiatives. You will manage day-to-day data operations, lead a team of data professionals, and drive process excellence across data intake, transformation, validation, and delivery. Close collaboration with data engineering, analytics, IT, governance, and business stakeholders is essential 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 downstream systems. Define and implement standard operating procedures for data lifecycle management, ensuring data accuracy, completeness, and integrity. Oversee and continuously improve daily operational workflows including scheduling, monitoring, and troubleshooting data jobs in cloud and on-prem environments. Establish and track key data operations metrics (SLAs, throughput, latency, data quality, incident resolution) and drive ongoing improvements. Partner with data engineering and platform teams to optimize pipelines, support new data integrations, and ensure scalable, resilient operational data flows. Collaborate with data governance, compliance, and security teams to maintain regulatory compliance, data privacy, and access controls. Act as 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, prioritize operational needs, and align with business objectives. Drive adoption of best practices for documentation, metadata, lineage, and change management across data operations. Mentor and develop a high-performing team of data operations analysts and leads. Must-Have Functional Skills Experience managing a team of data engineers in biotech/pharma companies. Expertise in designing and maintaining ETL data pipelines and analytics solutions from multiple sources. Hands-on experience with cloud platforms (especially AWS), including architecting scalable, cost-effective data solutions. Skilled in managing data workflows in cloud environments such as AWS, Azure, or GCP. Strong problem-solving skills to analyze complex data flow issues and implement sustainable solutions. Proficiency in SQL, Python, or scripting languages for monitoring and automation. Experience collaborating in matrixed organizations with data engineering, analytics, IT operations, and business teams. Familiarity with data governance, metadata management, access controls, and regulatory standards (e.g., GDPR, HIPAA, SOX). Excellent leadership, communication, and stakeholder engagement skills. Knowledgeable in full stack development, DataOps automation, logging frameworks, and pipeline orchestration tools. Strong analytical and interpersonal communication abilities. Good-to-Have Skills Data Engineering management experience in Biotech/Life Sciences/Pharma sectors. Experience with graph databases such as Stardog, Marklogic, Neo4J, or Allegrograph. Education and Professional Certifications 9 to 12 years of experience in Computer Science, IT, or related fields. AWS Certified Data Engineer (preferred). Databricks Certificate (preferred). Scaled Agile SAFe certification (preferred). Soft Skills Excellent analytical and troubleshooting capabilities. 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.

Exp - 6 to 10 yrs Loc - Bengaluru Pref - Local candidates Mode - Hybrid (2 days from office) Posi - Permanent FTE Must have skills - Java, Spring boot, Multithreading, Logging, Angular, Kafka & Azure cloud

ABOUT THE ROLE Role Description: We are seeking a seasoned Senior Engineering Manager (Data Engineering) to drive the development & maintenance of data pipelines developed by data engineering teams focusing on deep domain expertise of HR/Finance data. This role will lead a team of data engineers who will be maintaining data pipelines, and operational frameworks that support enterprise-wide data solutions. The ideal candidate will drive best practices in data engineering, cloud technologies, and Agile development, ensuring robust governance, data quality, and efficiency. The role requires technical expertise, operational excellence and a deep understanding of data solutions to optimize data-driven decision-making. Roles & Responsibilities: Lead and mentor a high performing team of data engineers who will be developing and maintaining the complex data pipelines. Drive the development of data tools and frameworks for managing and accessing data efficiently across the organization. Oversee the implementation of performance monitoring protocols across data pipelines, ensuring real-time visibility, alerts, and automated recovery mechanisms. Coach engineers in building dashboards and aggregations to monitor pipeline health and detect inefficiencies, ensuring optimal performance and cost-effectiveness. Lead the implementation of self-healing solutions, reducing failure points and improving pipeline stability and efficiency across multiple product features. Oversee data governance strategies, ensuring compliance with security policies, regulations, and data accessibility best practices. Guide engineers in data modeling, metadata management, and access control, ensuring structured data handling across various business use cases. Collaborate with business leaders, product owners, and cross-functional teams to ensure alignment of data architecture with product requirements and business objectives. Prepare team members for stakeholder discussions by helping assess data costs, access requirements, dependencies, and availability for business scenarios. 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. Functional Skills: Must-Have Skills: Experience managing a team of data engineers in biotech/pharma domain companies. Experience in designing and maintaining data 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. Proficiency in Python, PySpark, SQL. Experience with dimensional data modeling. Experience working with Apache Spark, Apache Airflow. 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. Experienced with AWS or GCP or Azure cloud services. Understanding of end-to-end project/product life cycle. Well versed with full stack development & DataOps automation, logging 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 -15 years of experience in Computer Science, IT or related field AWS Certified Data Engineer preferred Databricks Certificate preferred 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 EQUAL OPPORTUNITY STATEMENT Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status. We will ensure that individuals with disabilities are provided with reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.

Data Engineering Manager What you will do Let’s do this. Let’s change the world. In this vital role you will lead a team of data engineers to build, optimize, and maintain scalable data architectures, data pipelines, and operational frameworks that support real-time analytics, AI-driven insights, and enterprise-wide data solutions. As a strategic leader, the ideal candidate will drive best practices in data engineering, cloud technologies, and Agile development, ensuring robust governance, data quality, and efficiency. The role requires technical expertise, team leadership, and a deep understanding of cloud data solutions to optimize data-driven decision-making. Lead and mentor a team of data engineers, fostering a culture of innovation, collaboration, and continuous learning for solving complex problems of R&D division. Oversee the development of data extraction, validation, and transformation techniques, ensuring ingested data is of high quality and compatible with downstream systems. Guide the team in writing and validating high-quality code for data ingestion, processing, and transformation, ensuring resiliency and fault tolerance. Drive the development of data tools and frameworks for running and accessing data efficiently across the organization. Oversee the implementation of performance monitoring protocols across data pipelines, ensuring real-time visibility, alerts, and automated recovery mechanisms. Coach engineers in building dashboards and aggregations to monitor pipeline health and detect inefficiencies, ensuring optimal performance and cost-effectiveness. Lead the implementation of self-healing solutions, reducing failure points and improving pipeline stability and efficiency across multiple product features. Oversee data governance strategies, ensuring compliance with security policies, regulations, and data accessibility best practices. Guide engineers in data modeling, metadata management, and access control, ensuring structured data handling across various business use cases. Collaborate with business leaders, product owners, and cross-functional teams to ensure alignment of data architecture with product requirements and business objectives. Prepare team members for key partner discussions by helping assess data costs, access requirements, dependencies, and availability for business scenarios. Drive Agile and Scaled Agile (SAFe) methodologies, handling 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 uses the latest innovations in data engineering and architecture. What we expect of you We are all different, yet we all use our unique contributions to serve patients. We are seeking a seasoned Engineering Manager (Data Engineering) to drive the development and implementation of our data strategy with deep expertise in R&D of Biotech or Pharma domain. Basic Qualifications: Doctorate degree OR Master’s degree and 4 to 6 years of experience in Computer Science, IT or related field OR Bachelor’s degree and 6 to 8 years of experience in Computer Science, IT or related field OR Diploma and 10 to 12 years of experience in Computer Science, IT or related field Experience leading a team of data engineers in the R&D domain of biotech/pharma companies. Experience architecting and building data and analytics solutions that extract, transform, and load data from multiple source systems. Data Engineering experience in R&D for Biotechnology or pharma industry Demonstrated hands-on experience with cloud platforms (AWS) and the ability to architect cost-effective and scalable data solutions. Proficiency in Python, PySpark, SQL. Experience with dimensional data modeling. Experience working with Apache Spark, Apache Airflow. 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. Experienced with AWS or GCP or Azure cloud services. Understanding of end to end project/product life cycle Well versed with full stack development & DataOps automation, logging 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. Preferred Qualifications: AWS Certified Data Engineer preferred Databricks Certificate preferred Scaled Agile SAFe certification preferred Project Management certifications preferred Data Engineering Management experience in Biotech/Pharma is a plus Experience using graph databases such as Stardog or Marklogic or Neo4J or Allegrograph, etc. 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 handle multiple priorities successfully Team-oriented, with a focus on achieving team goals Strong presentation and public speaking skills What you can expect of us As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way. In addition to the base salary, Amgen offers competitive and comprehensive Total Rewards Plans that are aligned with local industry standards. Apply now and make a lasting impact with the Amgen team. careers.amgen.com As an organization dedicated to improving the quality of life for people around the world, Amgen fosters an inclusive environment of diverse, ethical, committed and highly accomplished people who respect each other and live the Amgen values to continue advancing science to serve patients. Together, we compete in the fight against serious disease. Amgen is an Equal Opportunity employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

We are looking for a QA Engineer to join our rapidly growing team. Responsibilities Conducting manual testing; Creation of test cases for regression testing; Writing documentation. Perform internal and/or external demo sessions Requirements experience: 3+ years Understanding of software development methodologies; Ability to write understandable test documentation (test cases, test reports, defects); Experience in Web-technologies (xml, css, html, http); Experience in work with Task Management Systems (Jira); Knowledge of SQL (the ability to use Select 's and simple Join' s); Experience in work with logging systems Experience in work with software versions control systems Strong skills in black-box and white-box test design techniques Experience in API testing English level - Intermediate (good communication skills and ability to pass interviews). Optional Bonus Skills: Knowledge of # (other PL), experience in work with ELK. English: b2. 5 Days working - Monday to Friday. Saturday Sunday Off To Apply for this position send resumes at swarnalaxmi.shetty@solbeg.com