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Mid-level Bangalore data engineer with common experience range but specialized Databricks skills, moderate competition.
Heavy Databricks, Azure, Delta Lake and governance requirements raise background sensitivity (low transferability).
Explicit 5–8 years plus mandatory Databricks, Spark, governance and pipeline expertise makes screening strict.
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Design, build, and maintain scalable ETL/ELT pipelines on Azure Databricks following the medallion architecture ensuring reliability, performance, and timeliness.
Manage Unity Catalog and implement data governance strategies for access control, classification, lineage, and data-quality frameworks across the lakehouse environment.
Develop audit and operational-metadata tables for pipeline observability, troubleshoot data pipeline issues, and deliver curated analytics-ready datasets for BI and AI/ML applications.
Bachelor’s degree in Information Technology, Computer Science, Data Science, Analytics, or Statistics required.
5–8 years of hands-on experience in data engineering or analytics engineering with substantial experience on Databricks.
Proficient in Azure Databricks components, Spark/PySpark, SQL, and building medallion architecture pipelines (bronze, silver, gold, platinum).
Experience with Unity Catalog governance, Azure Data Lake Storage Gen2 integration, data orchestration (Databricks Workflows/Jobs, DLT), and CI/CD (Git, Databricks Asset Bundles).
Experienced with cross-cloud data migration and integration into Databricks lakehouse from platforms like AWS, GCP, Snowflake, or Redshift.
Has deep knowledge of data governance, data-quality frameworks supporting AI and advanced analytics use cases within enterprise environments.
Comfortable working on complex data models optimized for downstream AI/ML workloads and familiar with managing audit and control frameworks for data pipelines.