491 Data Pipeline Jobs - Page 7

Company Overview : Zorba Consulting India is a leading consultancy firm focused on delivering innovative solutions and strategies to enhance business performance. With a commitment to excellence, we prioritize collaboration, integrity, and customer-centric values in our operations. Our mission is to empower organizations by transforming data into actionable insights and enabling data-driven decision-making. We are dedicated to fostering a culture of continuous improvement and supporting our team members' professional development. Role Responsibilities : - Design and implement data pipelines using MS Fabric. - Develop data models to support business intelligence and analytics. - Manage and optimize ETL processes for data extraction, transformation, and loading. - Collaborate with cross-functional teams to gather and define data requirements. - Ensure data quality and integrity in all data processes. - Implement best practices for data management, storage, and processing. - Conduct performance tuning for data storage and retrieval for enhanced efficiency. - Generate and maintain documentation for data architecture and data flow. - Participate in troubleshooting data-related issues and implement solutions. - Monitor and optimize cloud-based solutions for scalability and resource efficiency. - Evaluate emerging technologies and tools for potential incorporation in projects. - Assist in designing data governance frameworks and policies. - Provide technical guidance and support to junior data engineers. - Participate in code reviews and ensure adherence to coding standards. - Stay updated with industry trends and best practices in data engineering. Qualifications : - 8+ years of experience in data engineering roles. - Strong expertise in MS Fabric and related technologies. - Proficiency in SQL and relational database management systems. - Experience with data warehousing solutions and data modeling. - Hands-on experience in ETL tools and processes. - Knowledge of cloud computing platforms (Azure, AWS, GCP). - Familiarity with Python or similar programming languages. - Ability to communicate complex concepts clearly to non-technical stakeholders. - Experience in implementing data quality measures and data governance. - Strong problem-solving skills and attention to detail. - Ability to work independently in a remote environment. - Experience with data visualization tools is a plus. - Excellent analytical and organizational skills. - Bachelor's degree in Computer Science, Engineering, or related field. - Experience in Agile methodologies and project management.

About the Job : We are seeking a highly skilled and experienced Senior Data Engineer to join our dynamic team. In this pivotal role, you will be instrumental in driving our data engineering initiatives, with a strong emphasis on leveraging Dataiku's capabilities to enhance data processing and analytics. You will be responsible for designing, developing, and optimizing robust data pipelines, ensuring seamless integration of diverse data sources, and maintaining high data quality and accessibility to support our business intelligence and advanced analytics projects. This role requires a unique blend of expertise in traditional data engineering principles, advanced data modeling, and a forward-thinking approach to integrating cutting-AI technologies, particularly LLM Mesh for Generative AI applications. If you are passionate about building scalable data solutions and are eager to explore the cutting edge of AI, we encourage you to apply. Key Responsibilities : - Dataiku Leadership : Drive data engineering initiatives with a strong emphasis on leveraging Dataiku capabilities for data preparation, analysis, visualization, and the deployment of data solutions. - Data Pipeline Development : Design, develop, and optimize robust and scalable data pipelines to support various business intelligence and advanced analytics projects. This includes developing and maintaining ETL/ELT processes to automate data extraction, transformation, and loading from diverse sources. - Data Modeling & Architecture : Apply expertise in data modeling techniques to design efficient and scalable database structures, ensuring data integrity and optimal performance. - ETL/ELT Expertise : Implement and manage ETL processes and tools to ensure efficient and reliable data flow, maintaining high data quality and accessibility. - Gen AI Integration : Explore and implement solutions leveraging LLM Mesh for Generative AI applications, contributing to the development of innovative AI-powered features. - Programming & Scripting : Utilize programming languages such as Python and SQL for data manipulation, analysis, automation, and the development of custom data solutions. - Cloud Platform Deployment : Deploy and manage scalable data solutions on cloud platforms such as AWS or Azure, leveraging their respective services for optimal performance and cost-efficiency. - Data Quality & Governance : Ensure seamless integration of data sources, maintaining high data quality, consistency, and accessibility across all data assets. Implement data governance best practices. - Collaboration & Mentorship : Collaborate closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver impactful solutions. Potentially mentor junior team members. - Performance Optimization : Continuously monitor and optimize the performance of data pipelines and data systems. Required Skills & Experience : - Proficiency in Dataiku : Demonstrable expertise in Dataiku for data preparation, analysis, visualization, and building end-to-end data pipelines and applications. - Expertise in Data Modeling : Strong understanding and practical experience in various data modeling techniques (e.g., dimensional modeling, Kimball, Inmon) to design efficient and scalable database structures. - ETL/ELT Processes & Tools : Extensive experience with ETL/ELT processes and a proven track record of using various ETL tools (e.g., Dataiku's built-in capabilities, Apache Airflow, Talend, SSIS, etc.). - Familiarity with LLM Mesh : Familiarity with LLM Mesh or similar frameworks for Gen AI applications, understanding its concepts and potential for integration. - Programming Languages : Strong proficiency in Python for data manipulation, scripting, and developing data solutions. Solid command of SQL for complex querying, data analysis, and database interactions. - Cloud Platforms : Knowledge and hands-on experience with at least one major cloud platform (AWS or Azure) for deploying and managing scalable data solutions (e.g., S3, EC2, Azure Data Lake, Azure Synapse, etc.). - Gen AI Concepts : Basic understanding of Generative AI concepts and their potential applications in data engineering. - Problem-Solving : Excellent analytical and problem-solving skills with a keen eye for detail. - Communication : Strong communication and interpersonal skills to collaborate effectively with cross-functional teams. Bonus Points (Nice to Have) : - Experience with other big data technologies (e.g., Spark, Hadoop, Snowflake). - Familiarity with data governance and data security best practices. - Experience with MLOps principles and tools. - Contributions to open-source projects related to data engineering or AI. Education : Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field.

About the Job : We are seeking a highly skilled and experienced Senior Data Engineer to join our dynamic team. In this pivotal role, you will be instrumental in driving our data engineering initiatives, with a strong emphasis on leveraging Dataiku's capabilities to enhance data processing and analytics. You will be responsible for designing, developing, and optimizing robust data pipelines, ensuring seamless integration of diverse data sources, and maintaining high data quality and accessibility to support our business intelligence and advanced analytics projects. This role requires a unique blend of expertise in traditional data engineering principles, advanced data modeling, and a forward-thinking approach to integrating cutting-AI technologies, particularly LLM Mesh for Generative AI applications. If you are passionate about building scalable data solutions and are eager to explore the cutting edge of AI, we encourage you to apply. Key Responsibilities : - Dataiku Leadership : Drive data engineering initiatives with a strong emphasis on leveraging Dataiku capabilities for data preparation, analysis, visualization, and the deployment of data solutions. - Data Pipeline Development : Design, develop, and optimize robust and scalable data pipelines to support various business intelligence and advanced analytics projects. This includes developing and maintaining ETL/ELT processes to automate data extraction, transformation, and loading from diverse sources. - Data Modeling & Architecture : Apply expertise in data modeling techniques to design efficient and scalable database structures, ensuring data integrity and optimal performance. - ETL/ELT Expertise : Implement and manage ETL processes and tools to ensure efficient and reliable data flow, maintaining high data quality and accessibility. - Gen AI Integration : Explore and implement solutions leveraging LLM Mesh for Generative AI applications, contributing to the development of innovative AI-powered features. - Programming & Scripting : Utilize programming languages such as Python and SQL for data manipulation, analysis, automation, and the development of custom data solutions. - Cloud Platform Deployment : Deploy and manage scalable data solutions on cloud platforms such as AWS or Azure, leveraging their respective services for optimal performance and cost-efficiency. - Data Quality & Governance : Ensure seamless integration of data sources, maintaining high data quality, consistency, and accessibility across all data assets. Implement data governance best practices. - Collaboration & Mentorship : Collaborate closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver impactful solutions. Potentially mentor junior team members. - Performance Optimization : Continuously monitor and optimize the performance of data pipelines and data systems. Required Skills & Experience : - Proficiency in Dataiku : Demonstrable expertise in Dataiku for data preparation, analysis, visualization, and building end-to-end data pipelines and applications. - Expertise in Data Modeling : Strong understanding and practical experience in various data modeling techniques (e.g., dimensional modeling, Kimball, Inmon) to design efficient and scalable database structures. - ETL/ELT Processes & Tools : Extensive experience with ETL/ELT processes and a proven track record of using various ETL tools (e.g., Dataiku's built-in capabilities, Apache Airflow, Talend, SSIS, etc.). - Familiarity with LLM Mesh : Familiarity with LLM Mesh or similar frameworks for Gen AI applications, understanding its concepts and potential for integration. - Programming Languages : Strong proficiency in Python for data manipulation, scripting, and developing data solutions. Solid command of SQL for complex querying, data analysis, and database interactions. - Cloud Platforms : Knowledge and hands-on experience with at least one major cloud platform (AWS or Azure) for deploying and managing scalable data solutions (e.g., S3, EC2, Azure Data Lake, Azure Synapse, etc.). - Gen AI Concepts : Basic understanding of Generative AI concepts and their potential applications in data engineering. - Problem-Solving : Excellent analytical and problem-solving skills with a keen eye for detail. - Communication : Strong communication and interpersonal skills to collaborate effectively with cross-functional teams. Bonus Points (Nice to Have) : - Experience with other big data technologies (e.g., Spark, Hadoop, Snowflake). - Familiarity with data governance and data security best practices. - Experience with MLOps principles and tools. - Contributions to open-source projects related to data engineering or AI. Education : Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field.

About the Job : We are seeking a highly skilled and experienced Senior Data Engineer to join our dynamic team. In this pivotal role, you will be instrumental in driving our data engineering initiatives, with a strong emphasis on leveraging Dataiku's capabilities to enhance data processing and analytics. You will be responsible for designing, developing, and optimizing robust data pipelines, ensuring seamless integration of diverse data sources, and maintaining high data quality and accessibility to support our business intelligence and advanced analytics projects. This role requires a unique blend of expertise in traditional data engineering principles, advanced data modeling, and a forward-thinking approach to integrating cutting-AI technologies, particularly LLM Mesh for Generative AI applications. If you are passionate about building scalable data solutions and are eager to explore the cutting edge of AI, we encourage you to apply. Key Responsibilities : - Dataiku Leadership : Drive data engineering initiatives with a strong emphasis on leveraging Dataiku capabilities for data preparation, analysis, visualization, and the deployment of data solutions. - Data Pipeline Development : Design, develop, and optimize robust and scalable data pipelines to support various business intelligence and advanced analytics projects. This includes developing and maintaining ETL/ELT processes to automate data extraction, transformation, and loading from diverse sources. - Data Modeling & Architecture : Apply expertise in data modeling techniques to design efficient and scalable database structures, ensuring data integrity and optimal performance. - ETL/ELT Expertise : Implement and manage ETL processes and tools to ensure efficient and reliable data flow, maintaining high data quality and accessibility. - Gen AI Integration : Explore and implement solutions leveraging LLM Mesh for Generative AI applications, contributing to the development of innovative AI-powered features. - Programming & Scripting : Utilize programming languages such as Python and SQL for data manipulation, analysis, automation, and the development of custom data solutions. - Cloud Platform Deployment : Deploy and manage scalable data solutions on cloud platforms such as AWS or Azure, leveraging their respective services for optimal performance and cost-efficiency. - Data Quality & Governance : Ensure seamless integration of data sources, maintaining high data quality, consistency, and accessibility across all data assets. Implement data governance best practices. - Collaboration & Mentorship : Collaborate closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver impactful solutions. Potentially mentor junior team members. - Performance Optimization : Continuously monitor and optimize the performance of data pipelines and data systems. Required Skills & Experience : - Proficiency in Dataiku : Demonstrable expertise in Dataiku for data preparation, analysis, visualization, and building end-to-end data pipelines and applications. - Expertise in Data Modeling : Strong understanding and practical experience in various data modeling techniques (e.g., dimensional modeling, Kimball, Inmon) to design efficient and scalable database structures. - ETL/ELT Processes & Tools : Extensive experience with ETL/ELT processes and a proven track record of using various ETL tools (e.g., Dataiku's built-in capabilities, Apache Airflow, Talend, SSIS, etc.). - Familiarity with LLM Mesh : Familiarity with LLM Mesh or similar frameworks for Gen AI applications, understanding its concepts and potential for integration. - Programming Languages : Strong proficiency in Python for data manipulation, scripting, and developing data solutions. Solid command of SQL for complex querying, data analysis, and database interactions. - Cloud Platforms : Knowledge and hands-on experience with at least one major cloud platform (AWS or Azure) for deploying and managing scalable data solutions (e.g., S3, EC2, Azure Data Lake, Azure Synapse, etc.). - Gen AI Concepts : Basic understanding of Generative AI concepts and their potential applications in data engineering. - Problem-Solving : Excellent analytical and problem-solving skills with a keen eye for detail. - Communication : Strong communication and interpersonal skills to collaborate effectively with cross-functional teams. Bonus Points (Nice to Have) : - Experience with other big data technologies (e.g., Spark, Hadoop, Snowflake). - Familiarity with data governance and data security best practices. - Experience with MLOps principles and tools. - Contributions to open-source projects related to data engineering or AI. Education : Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field.

As a Mid Databricks Engineer, you will play a pivotal role in designing, implementing, and optimizing data processing pipelines and analytics solutions on the Databricks platform. You will collaborate closely with cross-functional teams to understand business requirements, architect scalable solutions, and ensure the reliability and performance of our data infrastructure. This role requires deep expertise in Databricks, strong programming skills, and a passion for solving complex engineering challenges. What you'll do : - Design and develop data processing pipelines and analytics solutions using Databricks. - Architect scalable and efficient data models and storage solutions on the Databricks platform. - Collaborate with architects and other teams to migrate current solution to use Databricks. - Optimize performance and reliability of Databricks clusters and jobs to meet SLAs and business requirements. - Use best practices for data governance, security, and compliance on the Databricks platform. - Mentor junior engineers and provide technical guidance. - Stay current with emerging technologies and trends in data engineering and analytics to drive continuous improvement. You'll be expected to have : - Bachelor's or master's degree in computer science, Engineering, or a related field. - 5 to 8 years of overall experience and 2+ years of experience designing and implementing data solutions on the Databricks platform. - Proficiency in programming languages such as Python, Scala, or SQL. - Strong understanding of distributed computing principles and experience with big data technologies such as Apache Spark. - Experience with cloud platforms such as AWS, Azure, or GCP, and their associated data services. - Proven track record of delivering scalable and reliable data solutions in a fast-paced environment. - Excellent problem-solving skills and attention to detail. - Strong communication and collaboration skills with the ability to work effectively in cross-functional teams. - Good to have experience with containerization technologies such as Docker and Kubernetes. - Knowledge of DevOps practices for automated deployment and monitoring of data pipelines.

About the job : Experience : 6+ years as Azure Data Engineer including at least 1 E2E Implementation in Microsoft Fabric. Responsibilities : - Lead the design and implementation of Microsoft Fabric-centric data platforms and data warehouses. - Develop and optimize ETL/ELT processes within the Microsoft Azure ecosystem, effectively utilizing relevant Fabric solutions. - Ensure data integrity, quality, and governance throughout Microsoft Fabric environment. - Collaborate with stakeholders to translate business needs into actionable data solutions. - Troubleshoot and optimize existing Fabric implementations for enhanced performance. Skills : - Solid foundational knowledge in data warehousing, ETL/ELT processes, and data modeling (dimensional, normalized). - Design and implement scalable and efficient data pipelines using Data Factory (Data Pipeline, Data Flow Gen 2 etc) in Fabric, Pyspark notebooks, Spark SQL, and Python. This includes data ingestion, data transformation, and data loading processes. - Experience ingesting data from SAP systems like SAP ECC/S4HANA/SAP BW etc will be a plus. - Nice to have ability to develop dashboards or reports using tools like Power BI. Coding Fluency : - Proficiency in SQL, Python, or other languages for data scripting, transformation, and automation.

About the Job : We are seeking a highly skilled and experienced Senior Data Engineer to join our dynamic team. In this pivotal role, you will be instrumental in driving our data engineering initiatives, with a strong emphasis on leveraging Dataiku's capabilities to enhance data processing and analytics. You will be responsible for designing, developing, and optimizing robust data pipelines, ensuring seamless integration of diverse data sources, and maintaining high data quality and accessibility to support our business intelligence and advanced analytics projects. This role requires a unique blend of expertise in traditional data engineering principles, advanced data modeling, and a forward-thinking approach to integrating cutting-AI technologies, particularly LLM Mesh for Generative AI applications. If you are passionate about building scalable data solutions and are eager to explore the cutting edge of AI, we encourage you to apply. Key Responsibilities : - Dataiku Leadership : Drive data engineering initiatives with a strong emphasis on leveraging Dataiku capabilities for data preparation, analysis, visualization, and the deployment of data solutions. - Data Pipeline Development : Design, develop, and optimize robust and scalable data pipelines to support various business intelligence and advanced analytics projects. This includes developing and maintaining ETL/ELT processes to automate data extraction, transformation, and loading from diverse sources. - Data Modeling & Architecture : Apply expertise in data modeling techniques to design efficient and scalable database structures, ensuring data integrity and optimal performance. - ETL/ELT Expertise : Implement and manage ETL processes and tools to ensure efficient and reliable data flow, maintaining high data quality and accessibility. - Gen AI Integration : Explore and implement solutions leveraging LLM Mesh for Generative AI applications, contributing to the development of innovative AI-powered features. - Programming & Scripting : Utilize programming languages such as Python and SQL for data manipulation, analysis, automation, and the development of custom data solutions. - Cloud Platform Deployment : Deploy and manage scalable data solutions on cloud platforms such as AWS or Azure, leveraging their respective services for optimal performance and cost-efficiency. - Data Quality & Governance : Ensure seamless integration of data sources, maintaining high data quality, consistency, and accessibility across all data assets. Implement data governance best practices. - Collaboration & Mentorship : Collaborate closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver impactful solutions. Potentially mentor junior team members. - Performance Optimization : Continuously monitor and optimize the performance of data pipelines and data systems. Required Skills & Experience : - Proficiency in Dataiku : Demonstrable expertise in Dataiku for data preparation, analysis, visualization, and building end-to-end data pipelines and applications. - Expertise in Data Modeling : Strong understanding and practical experience in various data modeling techniques (e.g., dimensional modeling, Kimball, Inmon) to design efficient and scalable database structures. - ETL/ELT Processes & Tools : Extensive experience with ETL/ELT processes and a proven track record of using various ETL tools (e.g., Dataiku's built-in capabilities, Apache Airflow, Talend, SSIS, etc.). - Familiarity with LLM Mesh : Familiarity with LLM Mesh or similar frameworks for Gen AI applications, understanding its concepts and potential for integration. - Programming Languages : Strong proficiency in Python for data manipulation, scripting, and developing data solutions. Solid command of SQL for complex querying, data analysis, and database interactions. - Cloud Platforms : Knowledge and hands-on experience with at least one major cloud platform (AWS or Azure) for deploying and managing scalable data solutions (e.g., S3, EC2, Azure Data Lake, Azure Synapse, etc.). - Gen AI Concepts : Basic understanding of Generative AI concepts and their potential applications in data engineering. - Problem-Solving : Excellent analytical and problem-solving skills with a keen eye for detail. - Communication : Strong communication and interpersonal skills to collaborate effectively with cross-functional teams. Bonus Points (Nice to Have) : - Experience with other big data technologies (e.g., Spark, Hadoop, Snowflake). - Familiarity with data governance and data security best practices. - Experience with MLOps principles and tools. - Contributions to open-source projects related to data engineering or AI. Education : Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field.

Role Senior Databricks Engineer As a Mid Databricks Engineer, you will play a pivotal role in designing, implementing, and optimizing data processing pipelines and analytics solutions on the Databricks platform. You will collaborate closely with cross-functional teams to understand business requirements, architect scalable solutions, and ensure the reliability and performance of our data infrastructure. This role requires deep expertise in Databricks, strong programming skills, and a passion for solving complex engineering challenges. What you'll do : - Design and develop data processing pipelines and analytics solutions using Databricks. - Architect scalable and efficient data models and storage solutions on the Databricks platform. - Collaborate with architects and other teams to migrate current solution to use Databricks. - Optimize performance and reliability of Databricks clusters and jobs to meet SLAs and business requirements. - Use best practices for data governance, security, and compliance on the Databricks platform. - Mentor junior engineers and provide technical guidance. - Stay current with emerging technologies and trends in data engineering and analytics to drive continuous improvement. You'll be expected to have : - Bachelor's or master's degree in computer science, Engineering, or a related field. - 5 to 8 years of overall experience and 2+ years of experience designing and implementing data solutions on the Databricks platform. - Proficiency in programming languages such as Python, Scala, or SQL. - Strong understanding of distributed computing principles and experience with big data technologies such as Apache Spark. - Experience with cloud platforms such as AWS, Azure, or GCP, and their associated data services. - Proven track record of delivering scalable and reliable data solutions in a fast-paced environment. - Excellent problem-solving skills and attention to detail. - Strong communication and collaboration skills with the ability to work effectively in cross-functional teams. - Good to have experience with containerization technologies such as Docker and Kubernetes. - Knowledge of DevOps practices for automated deployment and monitoring of data pipelines.

About the job : Role : Microsoft Fabric Data Engineer Experience : 6+ years as Azure Data Engineer including at least 1 E2E Implementation in Microsoft Fabric. Responsibilities : - Lead the design and implementation of Microsoft Fabric-centric data platforms and data warehouses. - Develop and optimize ETL/ELT processes within the Microsoft Azure ecosystem, effectively utilizing relevant Fabric solutions. - Ensure data integrity, quality, and governance throughout Microsoft Fabric environment. - Collaborate with stakeholders to translate business needs into actionable data solutions. - Troubleshoot and optimize existing Fabric implementations for enhanced performance. Skills : - Solid foundational knowledge in data warehousing, ETL/ELT processes, and data modeling (dimensional, normalized). - Design and implement scalable and efficient data pipelines using Data Factory (Data Pipeline, Data Flow Gen 2 etc) in Fabric, Pyspark notebooks, Spark SQL, and Python. This includes data ingestion, data transformation, and data loading processes. - Experience ingesting data from SAP systems like SAP ECC/S4HANA/SAP BW etc will be a plus. - Nice to have ability to develop dashboards or reports using tools like Power BI. Coding Fluency : - Proficiency in SQL, Python, or other languages for data scripting, transformation, and automation.

About the job : Role : Microsoft Fabric Data Engineer Experience : 6+ years as Azure Data Engineer including at least 1 E2E Implementation in Microsoft Fabric. Responsibilities : - Lead the design and implementation of Microsoft Fabric-centric data platforms and data warehouses. - Develop and optimize ETL/ELT processes within the Microsoft Azure ecosystem, effectively utilizing relevant Fabric solutions. - Ensure data integrity, quality, and governance throughout Microsoft Fabric environment. - Collaborate with stakeholders to translate business needs into actionable data solutions. - Troubleshoot and optimize existing Fabric implementations for enhanced performance. Skills : - Solid foundational knowledge in data warehousing, ETL/ELT processes, and data modeling (dimensional, normalized). - Design and implement scalable and efficient data pipelines using Data Factory (Data Pipeline, Data Flow Gen 2 etc) in Fabric, Pyspark notebooks, Spark SQL, and Python. This includes data ingestion, data transformation, and data loading processes. - Experience ingesting data from SAP systems like SAP ECC/S4HANA/SAP BW etc will be a plus. - Nice to have ability to develop dashboards or reports using tools like Power BI. Coding Fluency : - Proficiency in SQL, Python, or other languages for data scripting, transformation, and automation.

Dear Candidate, We are looking for a skilled Data Engineer to design and maintain data pipelines, ensuring efficient data processing and storage. If you have expertise in ETL, SQL, and cloud-based data platforms, wed love to hear from you! Key Responsibilities: Design, develop, and maintain scalable data pipelines. Optimize data workflows for performance and efficiency. Work with structured and unstructured data sources. Implement data governance and security best practices. Collaborate with data scientists and analysts to support data-driven decisions. Ensure compliance with data privacy regulations (GDPR, CCPA). Required Skills & Qualifications: Proficiency in SQL, Python, or Scala for data processing. Experience with ETL tools (Informatica, Apache NiFi, AWS Glue). Hands-on experience with cloud data platforms (AWS, Azure, GCP). Knowledge of data warehousing (Snowflake, Redshift, BigQuery). Familiarity with Apache Spark, Kafka, or Hadoop for big data processing. Soft Skills: Strong problem-solving and analytical skills. Ability to work independently and in a team. Good communication skills to collaborate with stakeholders. Note: If interested, please share your updated resume and your preferred contact details. If shortlisted, our HR team will reach out to you. Kandi Srinivasa Reddy Delivery Manager Integra Technologies

Exp required 7+ yrs with data governance informatica tool Key Responsibilities: Data Governance Framework Development : Develop, implement, and maintain data governance frameworks, policies, and standards to ensure high-quality, consistent, and secure data across the organization. Collaborate with business units and stakeholders to define and enforce data governance policies, ensuring alignment with business goals and regulatory requirements. Data Quality Management : Define and enforce data quality standards, monitoring key data quality metrics. Identify, analyze, and resolve data quality issues across various data sources and platforms. Work with cross-functional teams to implement data quality improvement initiatives. Data Lineage & Metadata Management : Implement and maintain data lineage and metadata management solutions to ensure visibility and traceability of data throughout its lifecycle. Work with data architects and engineers to establish and document data flows, transformations, and dependencies. Data Security & Compliance : Ensure that data governance practices comply with relevant regulatory requirements (e.g., GDPR, CCPA, HIPAA). Implement data security controls to protect sensitive data and manage access to sensitive information. Stakeholder Collaboration : Partner with data architects, data engineers, data scientists, and business analysts to ensure alignment between technical and business needs for data governance. Provide training and support for teams on data governance policies, best practices, and tools. Data Governance Tools & Technologies : Lead the implementation and optimization of data governance tools and platforms. Continuously evaluate emerging tools and technologies to improve data governance processes. Reporting & Documentation : Develop and maintain comprehensive data governance documentation and reports. Provide regular updates to senior management on the status of data governance initiatives, risks, and areas of improvement. Requirements: Experience : 7+ years of experience in data governance, data management, or related fields. Proven track record in implementing data governance frameworks and policies at an enterprise level. In-depth knowledge of data governance concepts, including data quality, data lineage, metadata management, and data security. Technical Skills : Experience with data governance tools such as Collibra, Informatica, Alation, or similar. Strong understanding of databases, data warehousing, and big data platforms (e.g., Hadoop, Spark). Familiarity with data integration, ETL processes, and data modeling. Proficiency in SQL and other scripting languages (e.g., Python, Shell). Regulatory Knowledge : Solid understanding of data privacy and compliance regulations (GDPR, CCPA, HIPAA, etc.). Ability to assess and mitigate compliance risks related to data handling. Soft Skills : Excellent communication and interpersonal skills. Strong problem-solving skills and the ability to collaborate across teams. Ability to manage multiple projects and deadlines in a fast-paced environment. Roles and Responsibilities Exp required 7+ yrs with data governance informatica tool Key Responsibilities: Data Governance Framework Development : Develop, implement, and maintain data governance frameworks, policies, and standards to ensure high-quality, consistent, and secure data across the organization. Collaborate with business units and stakeholders to define and enforce data governance policies, ensuring alignment with business goals and regulatory requirements. Data Quality Management : Define and enforce data quality standards, monitoring key data quality metrics. Identify, analyze, and resolve data quality issues across various data sources and platforms. Work with cross-functional teams to implement data quality improvement initiatives. Data Lineage & Metadata Management : Implement and maintain data lineage and metadata management solutions to ensure visibility and traceability of data throughout its lifecycle. Work with data architects and engineers to establish and document data flows, transformations, and dependencies. Data Security & Compliance : Ensure that data governance practices comply with relevant regulatory requirements (e.g., GDPR, CCPA, HIPAA). Implement data security controls to protect sensitive data and manage access to sensitive information. Stakeholder Collaboration : Partner with data architects, data engineers, data scientists, and business analysts to ensure alignment between technical and business needs for data governance. Provide training and support for teams on data governance policies, best practices, and tools. Data Governance Tools & Technologies : Lead the implementation and optimization of data governance tools and platforms. Continuously evaluate emerging tools and technologies to improve data governance processes. Reporting & Documentation : Develop and maintain comprehensive data governance documentation and reports. Provide regular updates to senior management on the status of data governance initiatives, risks, and areas of improvement. Requirements: Experience : 7+ years of experience in data governance, data management, or related fields. Proven track record in implementing data governance frameworks and policies at an enterprise level. In-depth knowledge of data governance concepts, including data quality, data lineage, metadata management, and data security. Technical Skills : Experience with data governance tools such as Collibra, Informatica, Alation, or similar. Strong understanding of databases, data warehousing, and big data platforms (e.g., Hadoop, Spark). Familiarity with data integration, ETL processes, and data modeling. Proficiency in SQL and other scripting languages (e.g., Python, Shell). Regulatory Knowledge : Solid understanding of data privacy and compliance regulations (GDPR, CCPA, HIPAA, etc.). Ability to assess and mitigate compliance risks related to data handling. Soft Skills : Excellent communication and interpersonal skills. Strong problem-solving skills and the ability to collaborate across teams. Ability to manage multiple projects and deadlines in a fast-paced environment.

SQL DEVELOPER: Design and implement relational database structures optimized for performance and scalability. Develop and maintain complex SQL queries, stored procedures, triggers, and functions. Optimize database performance through indexing, query tuning, and regular maintenance. Ensure data integrity, consistency, and security across multiple environments. Collaborate with cross-functional teams to integrate SQL databases with applications and reporting tools. Develop and manage ETL (Extract, Transform, Load) processes for data ingestion and transformation. Monitor and troubleshoot database performance issues. Automate routine database tasks using scripts and tools. Document database architecture, processes, and procedures for future reference. Stay updated with the latest SQL best practices and database technologies.Data Retrieval: SQL Developers must be able to query large and complex databases to extract relevant data for analysis or reporting. Data Transformation: They often clean, join, and reshape data using SQL to prepare it for downstream processes like analytics or machine learning. Performance Optimization: Writing queries that run efficiently is key, especially when dealing with big data or real-time systems. Understanding of Database Schemas: Knowing how tables relate and how to navigate normalized or denormalized structures is essential. QE: Design, develop, and execute test plans and test cases for data pipelines, ETL processes, and data platforms. Validate data quality, integrity, and consistency across various data sources and destinations. Automate data validation and testing using tools such as PyTest, Great Expectations, or custom Python/SQL scripts. Collaborate with data engineers, analysts, and product managers to understand data requirements and ensure test coverage. Monitor data pipelines and proactively identify data quality issues or anomalies. Contribute to the development of data quality frameworks and best practices. Participate in code reviews and provide feedback on data quality and testability. Strong SQL skills and experience with large-scale data sets. Proficiency in Python or another scripting language for test automation. Experience with data testing tools Familiarity with cloud platforms and data warehousing solutions

Utilizes software engineering principles to deploy and maintain fully automated data transformation pipelines that combine a large variety of storage and computation technologies to handle a distribution of data types and volumes in support of data architecture design. Key Responsibilities : A Data Engineer designs data products and data pipelines that are resilient to change, modular, flexible, scalable, reusable, and cost effective. - Design, develop, and maintain data pipelines and ETL processes using Microsoft Azure services (e.g., Azure Data Factory, Azure Synapse, Azure Databricks, Azure Fabric). - Utilize Azure data storage accounts for organizing and maintaining data pipeline outputs. (e.g., Azure Data Lake Storage Gen 2 & Azure Blob storage). - Collaborate with data scientists, data analysts, data architects and other stakeholders to understand data requirements and deliver high-quality data solutions. - Optimize data pipelines in the Azure environment for performance, scalability, and reliability. - Ensure data quality and integrity through data validation techniques and frameworks. - Develop and maintain documentation for data processes, configurations, and best practices. - Monitor and troubleshoot data pipeline issues to ensure timely resolution. - Stay current with industry trends and emerging technologies to ensure our data solutions remain cutting-edge. - Manage the CI/CD process for deploying and maintaining data solutions.

Role: AWS Data Engineer Experience: 6-9 years Location: Bangalore/ Chennai Mode of work: Onsite Interview: F2F required Key Skills: AWS Services, Talend, Python, AWS Glue, Apache Airflow, AWS SageMaker, Amazon Redshift, Terraform

- Architect and optimize distributed data processing pipelines leveraging PySpark for high-throughput, low-latency workloads. - Utilize the Apache big data stack (Hadoop, Hive, HDFS) to orchestrate ingestion, transformation, and governance of massive datasets. - Engineer fault-tolerant, production-grade ETL frameworks ensuring seamless scalability and system resilience. - Interface cross-functionally with Data Scientists and domain experts to translate analytical needs into performant data solutions. - Enforce rigorous data quality controls and lineage mechanisms to uphold auditability and regulatory compliance. - Contribute to core architectural design, implement clean and modular Python/Java code, and drive performance benchmarking at scale. Required Skills : - 5-7 years of experience. - Strong hands-on experience with PySpark for distributed data processing. - Deep understanding of Apache ecosystem (Hadoop, Hive, Spark, HDFS, etc.) - Solid grasp of data warehousing, ETL principles, and data modeling. - Experience working with large-scale datasets and performance optimization. - Familiarity with SQL and NoSQL databases. - Proficiency in Python and basic to intermediate knowledge of Java. - Experience in using version control tools like Git and CI/CD pipelines. Nice-to-Have Skills : - Working experience with Apache NiFi for data flow orchestration. - Experience in building real-time streaming data pipelines. - Knowledge of cloud platforms like AWS, Azure, or GCP. - Familiarity with containerization tools like Docker or orchestration tools like Kubernetes. If you are interested in the above roles and responsibilities, please share your updated resume along with the following details : Name as per Aadhar card.: Mobile Number : Alternative Mobile : Mail ID : Alternative Mail ID : Date of Birth : Total EXP : Relevant EXP : Current CTC : ECTC : Notice period(LWD) : Updated resume : Holding Offer(If any) : Interview availability : PF / UAN Number : Any Career /Education Gap:

Location : Remote Duration : Full-time Experience : 8+ years About The Role : We are seeking a highly skilled and experienced Senior PySpark/Python Developer to play a critical role in building a robust and reliable system for managing and disseminating customer notifications regarding PG&E's Planned Power Outages (PPOs). This is an exciting opportunity to tackle complex data challenges within a dynamic environment and contribute directly to improving customer communication and experience. As a key member of the team, you will be responsible for developing and implementing data processing pipelines that can ingest, transform, and synthesize information from various backend systems related to PPOs. Your primary goal will be to "eat ambiguity and excrete certainty" by taking complex, ever-changing data and producing clear, consistent, and timely notifications for PG&E customers. This role requires a strong individual contributor who can execute tasks within a defined scope while also demonstrating leadership qualities such as adaptability, continuous learning, and ownership. You should be comfortable navigating ambiguous situations, proactively identifying solutions, and providing direction even when clear, pre-defined solutions are not immediately apparent. Responsibilities : - Design, develop, and maintain scalable and efficient data processing pipelines using PySpark and Python. - Leverage your expertise in data engineering principles to build robust and reliable solutions. - Work with complex and dynamic datasets related to work requests and planned power outages. - Apply your knowledge of Palantir Foundry to integrate with existing data infrastructure and potentially build new modules or workflows. - Develop solutions to consolidate and finalize information about planned power outages from disparate systems. - Implement logic to ensure accurate and timely notifications are generated for customers, minimizing confusion and inconsistencies. - Identify and address edge cases and special circumstances within the PPO process. - Collaborate effectively with cross-functional teams, including business analysts, product owners, and other engineers. - Take ownership of technical challenges and drive them to resolution. - Proactively learn and adapt to new technologies and evolving requirements. - Contribute to the development of technical documentation and best practices. Required Skills And Experience : - Minimum of 8+ years of overall experience in software development with a strong focus on data engineering. - Extensive and demonstrable experience with PySpark for large-scale data processing. - Strong proficiency in Python and its relevant data engineering libraries. - Hands-on experience with Palantir Foundry and its core functionalities (e.g., Ontology, Pipelines, Actions, Contour). - Solid understanding of data modeling, data warehousing concepts, and ETL/ELT processes. - Experience working with complex and high-volume datasets. - Ability to write clean, efficient, and well-documented code. - Excellent problem-solving and analytical skills. - Strong communication and collaboration skills. - Ability to work independently and manage priorities effectively in a remote setting. - Demonstrated ability to take ownership and drive tasks to completion. - Comfort navigating ambiguous situations and proposing solutions. - A proactive and continuous learning mindset. Nice To Have : - Experience with cloud platforms (e.g., AWS, Azure, GCP). - Familiarity with data visualization tools. - Understanding of notification systems and best practices. - Prior experience in the utilities or energy sector.

Role : Senior Databricks Engineer / Databricks Technical Lead/ Data Architect. Experience : 8-15 years. Location : Bangalore, Chennai, Delhi, Pune. Primary Roles And Responsibilities : - Developing Modern Data Warehouse solutions using Databricks and AWS/ Azure Stack. - Ability to provide solutions that are forward-thinking in data engineering and analytics space. - Collaborate with DW/BI leads to understand new ETL pipeline development requirements. - Triage issues to find gaps in existing pipelines and fix the issues. - Work with business to understand the need in reporting layer and develop data model to fulfill reporting needs. - Help joiner team members to resolve issues and technical challenges. - Drive technical discussion with client architect and team members. - Orchestrate the data pipelines in scheduler via Airflow. Skills And Qualifications : - Bachelor's and/or masters degree in computer science or equivalent experience. - Must have total 6+ yrs of IT experience and 3+ years' experience in Data warehouse/ETL projects. - Deep understanding of Star and Snowflake dimensional modelling. - Strong knowledge of Data Management principles. - Good understanding of Databricks Data & AI platform and Databricks Delta Lake Architecture. - Should have hands-on experience in SQL, Python and Spark (PySpark). - Candidate must have experience in AWS/ Azure stack. - Desirable to have ETL with batch and streaming (Kinesis). - Experience in building ETL / data warehouse transformation processes. - Experience with Apache Kafka for use with streaming data / event-based data. - Experience with other Open-Source big data products Hadoop (incl. Hive, Pig, Impala). - Experience with Open Source non-relational / NoSQL data repositories (incl. MongoDB, Cassandra, Neo4J). - Experience working with structured and unstructured data including imaging & geospatial data. - Experience working in a Dev/Ops environment with tools such as Terraform, CircleCI, GIT. - Proficiency in RDBMS, complex SQL, PL/SQL, Unix Shell Scripting, performance tuning and troubleshoot. - Databricks Certified Data Engineer Associate/Professional Certification (Desirable). - Comfortable working in a dynamic, fast-paced, innovative environment with several ongoing concurrent projects. - Should have experience working in Agile methodology. - Strong verbal and written communication skills. - Strong analytical and problem-solving skills with a high attention to detail.

About the job : Exp :5+yrs NP : Imm-15 days Rounds : 3 Rounds (Virtual) Mandate Skills : Apache spark, hive, Hadoop, spark, scala, Databricks Job Description : The Role : - Designing and building optimized data pipelines using cutting-edge technologies in a cloud environment to drive analytical insights. - Constructing infrastructure for efficient ETL processes from various sources and storage systems. - Leading the implementation of algorithms and prototypes to transform raw data into useful information. - Architecting, designing, and maintaining database pipeline architectures, ensuring readiness for AI/ML transformations. - Creating innovative data validation methods and data analysis tools. - Ensuring compliance with data governance and security policies. - Interpreting data trends and patterns to establish operational alerts. - Developing analytical tools, programs, and reporting mechanisms - Conducting complex data analysis and presenting results effectively. - Preparing data for prescriptive and predictive modeling. - Continuously exploring opportunities to enhance data quality and reliability. - Applying strong programming and problem-solving skills to develop scalable solutions. Requirements : - Experience in the Big Data technologies (Hadoop, Spark, Nifi, Impala) - 5+ years of hands-on experience designing, building, deploying, testing, maintaining, monitoring, and owning scalable, resilient, and distributed data pipelines. - High proficiency in Scala/Java and Spark for applied large-scale data processing. - Expertise with big data technologies, including Spark, Data Lake, and Hive

Role & responsibilities Report and Dashboard Development: Design, develop, and deploy interactive Power BI reports and dashboards to meet the needs of various business units. Data Modeling: Develop and maintain data models to support business requirements, ensuring efficient data retrieval and reporting. Data Integration: Integrate data from various sources into SQL Server and Azure Synapse, ensuring data accuracy and consistency. Collaboration: Work closely with stakeholders, including business analysts, data scientists, and management, to gather requirements and deliver solutions that drive business insights. Documentation: Create and maintain comprehensive documentation for data models, reports, dashboards, and processes. Performance Monitoring: Monitor and optimize the performance of BI solutions, identifying and resolving issues proactively. Training and Support: Provide support to end-users on Power BI functionalities and best practices. Preferred candidate profile Excellent analytical, troubleshooting, problem-solving and research skills. Must be able to multitask and have experience with interacting within a diverse user/customer base 2-3 years of experience in BI development and data analysis. 3-5 years of experience with Power BI report development and deployment. 2-3 years of experience using SQL server and /or Azure Synapse analytics. Excellent written, verbal, and interpersonal communication skills 2-3 years of experience with Data Warehouse concepts, including the use of Extract, Transform, and Load (ETL) tools. Experience using Cloud architecture, NoSQL databases and R/Python is a plus. Experience using building data pipelines to integrate with unstructured data sources is a plus. Sales/Marketing business background is plus.

-Role & responsibilities := Build and enhance the core data platform , including ETL pipelines and warehouse layers Work with large volumes of structured and semi-structured data across PostgreSQL, MySQL, and MongoDB Design and maintain analytics-ready datasets to support operational, financial, and compliance reporting Write and optimize complex SQL queries to process billions of records efficiently Develop transformation logic and data workflows using Python or Groovy Ensure data quality, reliability, and performance across on-prem and AWS cloud environments Collaborate with engineering, analytics, and product teams to solve business problems using data Implement data validation, audit, and alerting mechanisms to maintain platform stability Drive data exploration to identify patterns, gaps, and opportunities for performance improvement (For Lead role) Provide technical direction and mentorship across the team, ensuring best practices in design and scalability Preferred candidate profile :- 4 - 8 years of experience in data engineering or backend systems Strong skills in SQL, Python/Groovy , and working with PostgreSQL, MySQL, MongoDB Hands-on with ETL pipelines , data warehousing , and AWS/cloud platforms Experience handling structured and semi-structured data Self-driven, flexible, and preferably from a startup or fast-paced environment For Lead role: prior team collaboration or mentoring experience is a plus

FIS Quantum/Echos Production Support Service Engineer Required Qualifications 5+ years experience with FIS Quantum and Echos treasury management systems Hands-on experience in static data maintenance within financial applications Proficiency in Oracle/SQL Server database concepts and SQL query authoring Experience authoring, troubleshooting and fixing Enterprise Workflow Framework (EWF) in Quantum Knowledge of treasury processes including In-House Banking, Cash forecasting, and Intercompany Settlement Familiarity with bank statement and cashflow interfaces Experience with Cash Xplorer configuration and troubleshooting Working knowledge of Golden Gate and Datapipelines technologies Understanding of SOX compliance requirements for financial systems Experience with FIS support tools including the FIS ticketing system This role requires a blend of financial systems knowledge and technical troubleshooting skills to maintain critical treasury management systems and provide exceptional support to business users. If interested or for more queries write to thirupavai.k@wipro.com

Pattern values data and engineering required to take full advantage of it. As a Senior Data Engineer at Pattern, you will be working on business problems that have a huge impact on how the company maintains its competitive edge. Essential Duties and Responsibilities Develop, deploy, and support real-time, automated, scalable data streams from a variety of sources into the data lake or data warehouse. Develop and implement data auditing strategies and processes to ensure data quality; identify and resolve problems associated with large-scale data processing workflows; implement technical solutions to maintain data pipeline processes and troubleshoot failures. Collaborate with technology teams and partners to specify data requirements and provide access to data. Tune application and query performance using profiling tools and SQL or other relevant query languages. Understand business, operations, and analytics requirements for data Build data expertise and own data quality for assigned areas of ownership Work with data infrastructure to triage issues and drive to resolution Required Qualifications Bachelors degree in data science, Data Analytics, Information Management, Computer Science, Information Technology, related field, or equivalent professional experience Overall experience should be more than 7 + years 3+ years of experience working with SQL 3+ years of experience in implementing modern data architecture-based data warehouses 2+ years of experience working with data warehouses such as Redshift, BigQuery, or Snowflake and understanding data architecture design Excellent software engineering and scripting knowledge Strong communication skills (both in presentation and comprehension) along with the aptitude for thought leadership in data management and analytics Expertise with data systems working with massive data sets from various data sources Ability to lead a team of Data Engineers Preferred Qualifications Experience working with time series databases Advanced knowledge of SQL , including the ability to write stored procedures, triggers, analytic/windowing functions, and tuning. Advanced knowledge of Snowflake, including the ability to write and orchestrate streams and tasks Background in Big Data, non-relational databases, Machine Learning and Data Mining Experience with cloud-based technologies including SNS , SQS , SES , S3 , Lambda , and Glue Experience with modern data platforms like Redshift , Cassandra , DynamoDB , Apache Airflow , Spark , or ElasticSearch Expertise in Data Quality and Data Governance Our Core Values Data Fanatics: Our edge is always found in the data Partner Obsessed: We are obsessed with partner success Team of Doers: We have a bias for action Game Changers: We encourage innovation

Velotio Technologies is a product engineering company working with innovative startups and enterprises. We are a certified Great Place to Work and recognized as one of the best companies to work for in India. We have provided full-stack product development for 110+ startups across the globe building products in the cloud-native, data engineering, B2B SaaS, IoT & Machine Learning space. Our team of 400+ elite software engineers solves hard technical problems while transforming customer ideas into successful products. Requirements Design, develop, and maintain robust and scalable data pipelines that ingest, transform, and load data from various sources into data warehouse. Collaborate with business stakeholders to understand data requirements and translate them into technical solutions. Implement data quality checks and monitoring to ensure data accuracy and integrity. Optimize data pipelines for performance and efficiency. Troubleshoot and resolve data pipeline issues. Stay up-to-date with emerging technologies and trends in data engineering. Qualifications Bachelors or Masters degree in Computer Science, Engineering, or a related field. 2+ years of experience in data engineering or a similar role. Strong proficiency in SQL and at least one programming language (e.g., Python, Java). Experience with data pipeline tools and frameworks Experience with cloud-based data warehousing solutions (Snowflake). Experience with AWS Kinesis, SNS, SQS Excellent problem-solving and analytical skills. Strong communication and interpersonal skills. Desired Skills & Experience: Data pipeline architecture Data warehousing ETL (Extract, Transform, Load) Data modeling SQL Python or Java or Go Cloud computing Business intelligence Our Culture : We have an autonomous and empowered work culture encouraging individuals to take ownership and grow quickly Flat hierarchy with fast decision making and a startup-oriented get things done culture A strong, fun & positive environment with regular celebrations of our success. We pride ourselves in creating an inclusive, diverse & authentic environment At Velotio, we embrace diversity. Inclusion is a priority for us, and we are eager to foster an environment where everyone feels valued. We welcome applications regardless of ethnicity or cultural background, age, gender, nationality, religion, disability or sexual orientation.

Description: Hiring Data Engineer with AWS or GCP Cloud Requirements: Role Summary: The Data Engineer will be responsible for designing, implementing, and maintaining the data infrastructure and pipelines necessary for AI/ML model training and deployment. They will work closely with data scientists and engineers to ensure data is clean, accessible, and efficiently processed Required Experience: • 6-8 years of experience in data engineering, ideally in financial services. • Strong proficiency in SQL, Python, and big data technologies (e.g., Hadoop, Spark). • Experience with cloud platforms (e.g., AWS, Azure, GCP) and data warehousing solutions. • Familiarity with ETL processes and tools. • Knowledge of data governance, security, and compliance best practices. Job Responsibilities: Key Responsibilities: • Build and maintain scalable data pipelines for data collection, processing, and analysis. • Ensure data quality and consistency for training and testing AI models. • Collaborate with data scientists and AI engineers to provide the required data for model development. • Optimize data storage and retrieval to support AI-driven applications. • Implement data governance practices to ensure compliance and security. What We Offer: Exciting Projects: We focus on industries like High-Tech, communication, media, healthcare, retail and telecom. Our customer list is full of fantastic global brands and leaders who love what we build for them. Collaborative Environment: You Can expand your skills by collaborating with a diverse team of highly talented people in an open, laidback environment — or even abroad in one of our global centers or client facilities! Work-Life Balance: GlobalLogic prioritizes work-life balance, which is why we offer flexible work schedules, opportunities to work from home, and paid time off and holidays. Professional Development: Our dedicated Learning & Development team regularly organizes Communication skills training(GL Vantage, Toast Master),Stress Management program, professional certifications, and technical and soft skill trainings. Excellent Benefits: We provide our employees with competitive salaries, family medical insurance, Group Term Life Insurance, Group Personal Accident Insurance , NPS(National Pension Scheme ), Periodic health awareness program, extended maternity leave, annual performance bonuses, and referral bonuses. Fun Perks: We want you to love where you work, which is why we host sports events, cultural activities, offer food on subsidies rates, Corporate parties. Our vibrant offices also include dedicated GL Zones, rooftop decks and GL Club where you can drink coffee or tea with your colleagues over a game of table and offer discounts for popular stores and restaurants!