Top Data Engineer Jobs in Chennai

Design, build, and maintain advanced data structures and pipelines for SAP HANA SPM. Implement inbound/outbound integrations with CRM/ERP and data warehouses, ensure data quality, reconciliation, governance, and auditability. Troubleshoot production issues, support UAT and post-deployment validation, document business rules and flows, and provide technical leadership and mentoring.

Design, build, and optimize scalable data pipelines and platforms (Snowflake, Databricks, Spark). Ensure data quality, lineage, and observability. Mentor junior engineers, lead cross-team initiatives, implement CI/CD, and improve internal data engineering tools and architectures.

Design, develop, and maintain data solutions and ETL pipelines using AWS Glue and PySpark. Ensure data quality, implement migrations, optimize workflows, mentor team members, and coordinate cross-functional data strategy and system performance improvements.

Design, build, and optimize scalable GCP-based data pipelines for batch and real-time processing. Develop ETL/ELT workflows using Python and PySpark, ingest data from various sources, and work with BigQuery, Dataproc, Dataflow, Cloud Storage, Pub/Sub, and Composer. Ensure data quality, performance tuning, monitoring, and automation while collaborating with architects, analysts, and data scientists to deliver production-ready cloud data solutions.

Design, build, and maintain scalable data pipelines and ETL processes using AWS Analytics. Ensure data quality, implement data architecture and models, collaborate with data scientists and analysts, and optimize data solutions for performance and reliability.

The role involves designing and building data systems using various technologies, especially within Microsoft Azure, and requires experience in system integration and data warehousing.

Senior data engineer responsible for designing, building, and optimizing scalable cloud-based data pipelines and lakehouse solutions on AWS. Lead complex integrations, implement data modeling and governance, promote engineering best practices, automate infrastructure and CI/CD, mentor engineers, and collaborate with stakeholders to deliver reliable analytics and BI solutions.

Design, build, and maintain AWS-native cloud data pipelines and ETL/ELT processes (S3, Glue, Redshift, AppFlow, Step Functions). Integrate enterprise apps (Salesforce, NetSuite), develop scalable data models, monitor pipeline performance, ensure data quality and governance, support CI/CD and IaC (Terraform), apply AI/ML for data quality/monitoring, and collaborate with stakeholders and peers.

Design and build scalable cloud-native data pipelines to ingest, clean, transform, chunk, embed, index, and serve enterprise data for LLM applications. Optimize vector database architectures, embedding generation, and RAG pipelines, collaborate with ML and software teams, implement Airflow orchestration, and ensure pipeline performance, monitoring, and reliability on AWS.

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.

The Senior Azure Data Engineer will design, develop, and maintain data infrastructure and pipelines, ensuring data quality, integrity, and alignment with business objectives. Responsibilities include collaborating with teams and establishing best practices for data engineering.

The role involves enhancing application systems, providing solutions, consulting with clients, and analyzing vulnerabilities in applications while advising lower-level analysts and ensuring compliance with regulations.

Design, build, and maintain Azure-based data infrastructure and pipelines. Implement data models and schemas, ensure data quality, optimize distributed storage and processing (Databricks/PySpark), and establish engineering best practices while partnering with cross-functional teams.

Migrate legacy data warehouse implementations to Databricks by interpreting T-SQL logic, converting transformations into optimized PySpark/Databricks pipelines, designing ETL/ELT jobs, performing data validation and testing, optimizing performance, documenting migration logic, and collaborating with architects and business teams.

Design, build, and operate scalable data engineering services and ingestion pipelines (CDC/streaming). Develop and optimize a centralized analytics warehouse, data models, and aggregation strategies. Build APIs, enforce multi-tenant security and data governance, monitor pipeline health, improve automation and observability, and ensure production reliability through strong engineering practices.

Lead a two-month project to assess the current data environment and design, build, and optimize a scalable cloud data warehouse. Deliver automated ETL/ELT pipelines, dimensional models, analytics-ready datasets, documentation, and handover while partnering with analysts and stakeholders for BI and reporting.

Design, build, and maintain scalable ETL/ELT data pipelines and integrations from multiple sources. Optimize performance and reliability, implement cloud-based data architectures (AWS/Azure/GCP), collaborate with data scientists and product teams, and enforce best practices for testing, automation, security, and documentation.

Maintain and monitor Python-based web scraping configurations, detect and resolve scraping issues, coordinate with stakeholders, produce reports, and build ETL pipelines to ingest and transform data into the data lake.

Design, build, and operationalize AI-enabled data pipelines and production-grade AI applications using Python and Snowflake. Prepare and transform large datasets, develop features and embeddings, support deployment and monitoring of ML/LLM models, optimize Snowflake for AI workloads, and collaborate with data science and engineering teams to deliver scalable APIs and RAG-enabled data workflows.

Design, develop, and maintain data platforms and ETL processes on cloud-based big data systems. Own architecture, write code and APIs, build automation and CI/CD pipelines, collaborate with product managers, perform code reviews, and support data integration, modeling, and BI solutions for global customers.

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.