Top Data Engineer Jobs in Chennai

Designs, develops, and maintains scalable data pipelines and storage solutions. Ensures data quality through validation and cleansing, transforms data for analysis, and optimizes engineering processes for performance and reliability. Collaborates with cross-functional stakeholders to understand requirements and deliver effective data solutions.

Design and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows for data engineering automation, implement data quality and monitoring controls, optimize workloads, and support testing, deployment, troubleshooting, and production operations. Collaborate with business, data engineering, and platform teams to identify automation opportunities and develop reusable frameworks.

Designs, develops, and maintains data solutions, pipelines, and ETL processes using PySpark. Ensures data quality, reliability, and integrity across large datasets and systems. Troubleshoots distributed data-processing issues, documents workflows, collaborates with cross-functional teams, contributes to solution development, and guides junior team members. The role requires independent execution, subject-matter expertise, and at least three years of PySpark experience.

Develops and leads Oracle PL/SQL and Informatica PowerCenter data engineering solutions. Responsibilities include designing ETL workflows, optimizing SQL and data processing, developing data pipelines, supporting data warehouses, reviewing code, resolving production issues, defining technical standards, and collaborating with architects and stakeholders. The role also includes testing, risk assessment, release support, data quality management, documentation, and mentoring while ensuring adherence to data governance and audit standards.

Design, develop, and maintain data solutions, pipelines, and ETL processes for large-scale environments. Ensure data quality, validate and migrate data across systems, troubleshoot pipeline issues, document workflows, and collaborate with cross-functional teams. The role also involves independently solving data problems, contributing as a subject matter expert, and mentoring junior team members.

Designs and leads large-scale data engineering solutions and ETL migrations to Google Cloud Platform. Responsibilities include gathering requirements, designing and deploying scalable data pipelines, migrating legacy JCL and ETL jobs, integrating data sources, monitoring workflows, troubleshooting production issues, performance tuning, testing, estimating development efforts, and coordinating with stakeholders and engineering teams.

Lead the design, modernization, migration, and operation of large-scale data warehouses, data lakes, analytics platforms, and ETL pipelines on GCP. Gather requirements, develop migration strategies, build and optimize scalable data workflows, troubleshoot production issues, coordinate deployments, and mentor engineering teams. The role also involves stakeholder collaboration, effort estimation, technical leadership, performance tuning, job monitoring, and client-facing consulting.

Designs and modernizes large-scale data engineering solutions on GCP. Responsibilities include gathering requirements, designing and migrating ETL systems, developing scalable data pipelines, integrating diverse data sources, deploying and monitoring workflows, troubleshooting production issues, and optimizing performance. The role also leads estimation, testing, code deployment, stakeholder collaboration, and migration from legacy JCL and ETL jobs to GCP-native services.

Develop and support scalable ETL/ELT data pipelines, data warehouses, and data lake solutions. The role requires advanced SQL, Snowflake and IDMC/IICS expertise, pipeline troubleshooting, data security, performance optimization, and experience with big data technologies including Kafka, Spark, and Hadoop. Responsibilities include full lifecycle data engineering, root cause analysis, data reconciliation, cloud platform integration, and collaboration with cross-functional teams using agile and project management practices.

Build and support scalable batch and streaming data pipelines, cloud-native data platforms, data lakes, warehouses, and real-time processing systems. Own data initiatives from solution design through deployment, monitoring, performance tuning, and production support. Develop ETL/ELT pipelines, data models, and integration frameworks while collaborating with product, analytics, platform, and DevOps teams. Troubleshoot production issues, improve reliability, and contribute to documentation and engineering best practices.

Design, build, and maintain scalable Azure-based data pipelines and systems (Databricks, Data Lake, Synapse, ADF). Collaborate with cross-functional teams, ensure data quality and integrity, optimize performance, monitor and troubleshoot systems, and stay current with industry trends to improve solutions.

Develop and support scalable data operations, ETL pipelines, SQL integrations, Azure services, Power Platform automations, and Power BI reporting. Responsibilities include Azure Data Factory pipeline development, Logic Apps and Power Automate workflows, Power BI modeling and publishing, Azure resource administration, governance, monitoring, troubleshooting, security controls, documentation, and performance optimization.

Designs and builds end-to-end data platforms on Google Cloud for regulated-sector clients. Responsibilities include developing reliable pipelines, modeling scalable data systems, optimizing performance and cost, ensuring data governance and regional compliance, and applying cloud networking and security practices. The role requires strong SQL and Python skills, client-facing communication, and ownership of project outcomes.

Execute large-scale data platform migrations to Snowflake, adapting approximately 1,000 datasets and existing pipelines. Develop, test, schedule, and monitor Airflow workflows while supporting migrations to Airflow 3, Iceberg, and new Confluent regions. Ensure data quality, reliability, performance, and timely completion of modernization initiatives through hands-on Python, SQL, Spark, Snowflake, Kafka, and AWS S3 engineering.

Supports data onboarding, pipeline monitoring, troubleshooting, maintenance, validation, metadata management, lineage documentation, and cloud platform operations. Develops Python-based ingestion and automation scripts, assists with AWS deployments and monitoring, participates in incident response, and documents operational procedures. Collaborates with senior engineers while working with Amazon S3, AWS Glue Data Catalog, CloudFormation, Databricks, and related data technologies.

Designs, builds, and maintains scalable batch and real-time data pipelines, warehouses, lakes, and models supporting analytics and machine learning. Responsibilities include data quality monitoring, database optimization, cloud infrastructure management, governance, security, CI/CD, documentation, and production troubleshooting. The role collaborates with data scientists, analysts, and product teams to deliver reliable datasets and infrastructure.

Provide L1-L3 production support for enterprise MongoDB Atlas environments, including monitoring, scaling, backups, upgrades, performance tuning, security, networking, disaster recovery, and incident resolution. Build Terraform-based infrastructure automation, support multi-cloud deployments across Azure, AWS, and GCP, partner with application and engineering teams, coordinate vendor escalations, and participate in 24/7 on-call coverage.

Designs, builds, and manages scalable clinical data pipelines supporting clinical trials. Responsibilities include ingesting, integrating, transforming, validating, and delivering data from EDC/eCRF, CTMS, and other source systems into data lakes and warehouses. The role requires Python, SQL, AWS, Spark, Airflow, APIs, and CI/CD expertise, along with clinical data workflow knowledge, data quality and traceability practices, and collaboration with technical and clinical stakeholders.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Designs and maintains scalable data pipelines and ETL/ELT workflows using Python, SQL, and Azure Databricks. Manages large structured and semi-structured datasets, data models, monitoring, quality controls, troubleshooting, and production support. Integrates LLM and Generative AI capabilities, including APIs, embeddings, prompt engineering, and RAG workflows, into data solutions. Collaborates with engineering, analytics, and business teams while following security, CI/CD, documentation, and maintainability best practices.

Design, build, and optimize GCP data ecosystems, including pipelines, data lakes, warehouses, storage architectures, and processing infrastructure. Gather requirements, model data, troubleshoot complex issues, tune performance, monitor systems, and support new functionality. Collaborate directly with clients and cross-functional teams, while mentoring junior data engineers and leading technical improvement discussions.

Provides technical leadership for Azure data engineering projects, including solution architecture, data modeling, pipelines, data integration, code quality, QA, performance optimization, governance, security, and documentation. Collaborates with stakeholders and project managers to define scope, manage risks, and deliver scalable, efficient solutions. Requires strong Python, SQL, PySpark, Databricks, Azure Data Factory, Delta Lake, Synapse, Spark, data warehousing, governance, and Azure DevOps expertise.

Builds and maintains scalable data pipelines, data architecture, ETL infrastructure, and analytics tools. The role assembles complex datasets, improves data delivery and infrastructure scalability, supports cross-functional stakeholders, and enables actionable business insights. Required expertise includes SAS, Python, Java, MATLAB, SQL and NoSQL databases, Hadoop-based data warehousing, MapReduce, Hive, and Presto. The position requires 5–8 years of related experience and a bachelor’s degree or equivalent experience.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

Leads the design, development, and maintenance of scalable ETL/ELT pipelines and data products across distributed, multi-cloud environments. Defines data engineering architectures, resolves complex production issues, and designs efficient data solutions. Provides technical leadership and mentorship while collaborating with architects, engineers, and business stakeholders. Requires expertise in big data, AWS services, data warehousing, databases, data modeling, orchestration, streaming, security, observability, version control, containers, and modern development practices.