Databricks Data Engineer Pune, India
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. The ideal candidate has strong hands-on experience across Databricks data engineering capabilities, with exposure to AI/ML features being a plus, while maintaining a core focus on scalable, reliable data pipelines and analytics workloads.
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.
Required Qualifications • Bachelor’s degree in Computer Science, Engineering, or a related field.
• 4 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.