Databricks Data Engineer

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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