Job Title: Databricks Data Engineer (Lakehouse Modernization - Migration)

Location: Pune, India (Hybrid)

Experience Required: 5 - 7 Years in Data Engineering

Notice Period: Immediate to 15 - 30 Days Max

Education Required: Bachelor's Degree in Computer Science, Information Technology, Engineering, or related field

About the Role:

We are seeking a Databricks Data Engineer to join our growing data engineering team in Pune, India. This role will play a key part in a large-scale modernization initiative to migrate a complex, enterprise-grade Microsoft SQL Server data warehouse ecosystem to the Databricks Lakehouse Platform.

Key Responsibilities:

- Design, build, and optimize scalable data pipelines using Databricks (Apache Spark, Delta Lake, Unity Catalog).

- Participate in the migration of a ~20TB compressed on-prem Microsoft SQL Server data warehouse to Databricks.

- Convert and modernize hundreds of SQL Server tables, thousands of SSIS jobs, and downstream SSRS/SSAS workloads.

- Re-engineer SSIS ETL processes into Databricks notebooks, workflows, and orchestration frameworks.

- Support migration or redesign of cube-based analytics (SSAS) into Databricks SQL, Delta tables, and modern semantic models.

- Implement data quality, validation, reconciliation, and audit controls during migration.

- Optimize performance and cost through efficient Spark usage, partitioning, and query tuning.

- Collaborate with analytics, BI, and AI/ML teams to enable downstream reporting and advanced analytics.

- Apply data governance, security, and access-control standards using Unity Catalog.

- Contribute to reusable frameworks, documentation, and platform best practices.

Qualifications:

- Bachelor's degree in Computer Science, Engineering, or a related field.

- 5 - 7 years of overall data engineering experience.

- Hands-on experience with Databricks on AWS (preferred); Azure experience acceptable.

- Strong proficiency in Spark (PySpark and/or Scala) and SQL.

- Proven experience migrating on-prem SQL Server data warehouses to cloud-based data platforms.

- Experience converting SSIS-based ETL pipelines into Spark-based data engineering solutions.

- Solid understanding of data warehousing concepts, dimensional modeling, and analytical workloads.

- Experience with Delta Lake, incremental processing patterns, and data versioning.

- Familiarity with Databricks Workflows, Jobs, and production-grade deployments.

- Practical experience with performance tuning and large-volume data processing.

Preferred Skills:

- Experience modernizing SSAS cube-based reporting solutions.

- Exposure to Databricks SQL Warehouses and BI integrations (Power BI preferred).

- Working knowledge of cloud-native data engineering practices aligned with Databricks best practices.

- Familiarity with MLflow, feature engineering, or AI-enablement within Databricks.

- Experience working in environments that follow Databricks-recommended data engineering patterns.

- Databricks certification is a plus.