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Metro Bangalore, mid-level Databricks data engineer with generalist skillset increases applicant competition.
Databricks/Azure specialization moderately limits industry portability but core data engineering skills remain transferable.
Explicit 5–8 years plus mandatory Databricks, Unity Catalog, streaming and governance experience narrows the pool.
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Design, build, and maintain scalable ETL/ELT data pipelines on Azure Databricks following the medallion architecture, ensuring reliable and performant data ingestion, transformation, and curation.
Manage data governance and security via Unity Catalog and implement audit, control, and operational-metadata systems to enable end-to-end observability and compliance.
Support AI/ML readiness by developing optimized Delta Lake data models and enable cross-cloud data sharing and migration within the Databricks lakehouse ecosystem.
Bachelor’s degree in Information Technology, Computer Science, Data Science, Analytics, or Statistics required.
5–8 years of hands-on experience in data engineering, data integration, or analytics engineering, including substantial experience with Azure Databricks.
Strong expertise in Spark/PySpark, SQL, and building medallion-architecture pipelines on Delta Lake.
Experience with Unity Catalog governance, cloud data migration/sharing (AWS, GCP, Snowflake, etc.), and data orchestration/CI-CD tools (Databricks Workflows/Jobs, DLT, Git).
Experienced working with enterprise-grade data governance, auditing, and data-quality frameworks within cloud data lakehouse environments.
Proficient in designing data pipelines and models that directly support AI/ML, advanced analytics, and BI consumption (Power BI, Tableau).
Skilled at independently managing Agile projects and technical delivery across complex data ecosystems involving multiple cloud platforms and modern data tools.