Top Data Engineer Jobs in Chennai

Build and maintain data structures, ETL pipelines, and ingestion frameworks; ensure data quality, lineage, and governance; manage warehouses/lakes and API access; support migrations, analytics model deployments, and collaborate to optimize data sourcing and processing.

Design, build, and maintain cloud-native data pipelines and ETL/ELT workflows for high-volume trade lifecycle data. Integrate DTCC sources (RTTM, Settlement Web) into a Unified Trade Record, process ISO 15022 messages, manage trade exception databases, optimize SQL Server performance, and ensure data quality, lineage, governance, and observability while collaborating with business stakeholders.

Design, develop, and support Python-based data pipelines, manage data transfers and onboarding, monitor pipeline health and SLAs, provide L2/L3 production support, troubleshoot incidents, document systems, and partner with stakeholders to translate requirements into scalable, automated data engineering solutions.

Design, build, and maintain data pipelines and ETL processes; ensure data quality and availability; collaborate with cross-functional teams to support analytics and reporting; implement data solutions and support data platform operations.

Lead database design, data modeling, migrations, and schema evolution across MySQL/Postgres and GCP databases. Design and operate config-driven ETL/ELT pipelines and streaming ingestion, enable CI/CD, troubleshoot data flows, and mentor offshore DB/ETL engineers.

Design and build scalable Azure-based data models, ETL pipelines, and data architectures. Implement data integration using Azure Data Factory/Databricks, ensure data quality, governance, security, and collaborate with data scientists and DevOps for deployment and CI/CD.

Design, implement and maintain high-volume online and offline batch and transactional data applications using Java and Spark. Build scalable data stores, write scripts, call APIs, and author complex SQL. Maintain code and database infrastructure, ensure data integrity, perform data cleaning, visualization and reporting, and support model training in an agile environment.

Design, develop, and maintain MSSQL and MySQL databases; write SQL/PLSQL, stored procedures, triggers, cursors, and schedulers. Troubleshoot jobs and performance, work with ETL tools (Talend/Fivetran/Pentaho) and document-oriented databases, support cloud database administration, enforce security, collaborate with developers/QA/DevOps, produce technical documentation, and potentially lead small projects.

Deliver data engineering and analysis work using Python, PySpark, SQL and data platforms (Airflow, Trino, Hive). Produce LLD, coordinate offshore teams, follow Agile/Scrum, and work with container platforms (Linux, OpenShift, Kubernetes) and visualization (Superset). Banking domain experience is a plus.

Design, build, and maintain scalable data pipelines and integrations across AWS, Azure, and BigQuery. Normalize, aggregate, and secure large web and transactional datasets, leveraging APIs, ETL and orchestration frameworks to support analytics and BI. Collaborate with cross-functional teams, optimize cloud data workflows for performance and cost, and ensure data quality for the e-commerce business.

Lead the design and implementation of ETL processes, develop Python solutions, and create data visualizations, collaborating with cross-functional teams.

Design, develop, and maintain data solutions and ETL pipelines. Ensure data quality, collaborate across teams, lead and mentor junior engineers, and drive team decisions.

Design, develop, and maintain data solutions and ETL pipelines using Databricks. Ensure data quality, perform data integration and modeling, collaborate with stakeholders, and optimize data processing for reliable analytics and deployment.

As an Azure Data Engineer, you will innovate and maintain data engineering services for Fortune clients, develop software for large projects, and utilize Azure components for solutions.

This role involves establishing and implementing application systems, focusing on database analysis, programming, and significant experience with Oracle PL/SQL and data modeling techniques.

Design, develop, and optimize AWS Glue ETL pipelines integrating data from S3, RDS, Redshift, and other AWS services. Transform data into analytics-ready formats, ensure data quality and security, tune performance, orchestrate workflows (Step Functions/Airflow), monitor jobs, and collaborate with analysts and stakeholders to deliver scalable data lake and warehouse solutions.

Design, build, and maintain ETL data pipelines using Talend and Snowflake. Extract data from APIs, reverse-engineer Alteryx workflows, apply Data Vault modeling, implement CI/CD and AWS data services, document designs, and collaborate with architects, SMEs, and developers to deliver robust cloud-based data warehousing solutions.

Principal Data Engineer responsible for building big-data solutions using PySpark and Databricks, writing and optimizing SQL, developing Python pipelines, and working with Hadoop/Hive, Git, Airflow/Dagster, and Azure.

Design and deliver scalable, highly available data architectures and pipelines (batch and streaming). Lead technical delivery, mentor teams, manage client engagements, ensure security/compliance, and drive implementation using cloud (AWS/Azure/GCP), ETL, data lake/warehouse, and best SDLC/Agile practices.

The Data Engineer will create and maintain data pipelines, optimize data delivery, build analytics tools, and support cross-functional teams with data infrastructure needs.

Design and develop data solutions using Power BI and Snowflake. Perform data mining, feature engineering, time-series and IoT analytics, build predictive models and automated anomaly detection, maintain data pipelines and quality, and collaborate in an agile team to deliver analytics integrated into product systems.

Design, build, and operate data pipelines and ETL workflows on GCP using BigQuery, Dataflow, Cloud Composer and Dataproc. Develop SQL-based analytics, implement data processing with Python/Java and PySpark, and apply Dataplex for data governance. Collaborate with teams to deliver scalable, production-ready data solutions.

The Data Engineer role involves data management, system design, and collaboration with teams to implement effective data solutions.

Design and implement modern data warehouse solutions using Databricks and cloud (Azure/AWS). Develop, orchestrate (Airflow) and troubleshoot ETL pipelines (batch and streaming), build dimensional data models for reporting, collaborate with architects and business stakeholders, mentor team members, and drive technical discussions.

Design, build, and optimize large-scale cloud data platforms and ETL/ELT pipelines on Azure. Implement data lakes, Delta Lake, Synapse, and Databricks solutions for batch and real-time processing, ensure data quality/governance, optimize SQL and analytics workloads, support CI/CD and deployments, and collaborate with stakeholders to deliver scalable data architectures and reporting.