Databricks Data Engineer
Summary
Designs and builds production-grade ETL/ELT pipelines and Lakehouse platforms on Azure/AWS Databricks using PySpark, Spark SQL, Delta Lake, and Unity Catalog, supporting BFSI modernisation, migration, and AI-ready data programs. Full-time, pan-India role for engineers with 5–10 years' experience including 3+ years hands-on Databricks.
We're Hiring | Databricks Data Engineer | Pan India | TCS
We are looking for experienced Databricks Data Engineers to join our growing team. The ideal candidate should have strong hands-on experience in PySpark/Spark SQL engineering, Delta Lake, and Unity Catalog governance to build reliable, high-performance, production-grade data products for BFSI modernisation, migration, and AI-ready data platform programs.
Location
Pan India
Experience
5–10 Years (role-dependent), including 3+ years hands-on Databricks
Employment Type
Full-time
Key Responsibilities
- Strong experience and understanding of Data Warehouse and Data Lake platform architecture and design.
- Design and develop scalable end-to-end ETL/ELT pipelines on Azure/AWS Databricks using PySpark, Spark SQL, and Delta Lake.
- Implement medallion (Bronze–Silver–Gold) Lakehouse architectures with automated ingestion, schema evolution, and performance optimisation.
- Build streaming and batch ingestion using Auto Loader, Lakeflow/Structured Streaming, Delta Live Tables (DLT), and Azure Data Factory.
- Enforce data governance, lineage, access control, and cataloguing through Unity Catalog, RBAC, and Delta Sharing.
- Optimise Spark workloads for cost and performance (partitioning, caching, cluster sizing, query tuning) across large datasets.
- Support legacy-to-Lakehouse migrations (Hadoop/DataStage/RDBMS) leveraging accelerators such as Lakebridge and reusable frameworks.
- Establish CI/CD for Databricks assets using Azure DevOps/Git, and build automated data validation and testing utilities.
- Collaborate with architects, AI/ML specialists, and client teams on solutioning, demos, PoCs, and production rollouts.
Mandatory Skills
- Azure/AWS Databricks (Notebooks, Workflows, Jobs)
- PySpark, Spark SQL, Python, SQL
- Delta Lake, Delta Live Tables (DLT), Auto Loader
- Unity Catalog, RBAC, data governance & lineage
- Medallion/Lakehouse architecture design
- ETL/ELT pipeline engineering & performance tuning
Good to Have
- Azure Data Factory (ADF), ADLS Gen2, AWS Glue
- Apache Airflow orchestration, DBT, streaming
- CI/CD — Azure DevOps, Git; DataOps practices
- Data migration & modernisation (Hadoop/RDBMS)
- Power BI/analytics enablement, Lakebase
- Exposure to GenAI/Agentic AI on Databricks
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