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Databricks-focused mid-level data role in Bengaluru; strong demand but specialized skills limit applicant pool.
Databricks Lakehouse and Spark expertise transfers across industries but requires cloud/data-platform background.
Multiple mandatory requirements: 6+ years data engineering, 2+ Databricks, PySpark, Delta Lake, Unity Catalog.
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Design, develop, and optimize scalable data pipelines and Lakehouse solutions on Databricks focusing on performance, cost, and governance.
Establish and enforce performance and governance standards including Unity Catalog for data access control, lineage, and security.
Lead mentoring of engineers, conduct design reviews, manage Databricks vendor relations, and drive AI-native operations and DevOps CI/CD best practices.
Bachelor's or Master's degree in Computer Science, Information Technology, or equivalent relevant experience.
6+ years of data engineering experience with at least 2+ years of hands-on Databricks experience in enterprise environments.
Deep expertise with Databricks Lakehouse, Delta Lake, Unity Catalog, Spark workload tuning, advanced Python (PySpark) and SQL skills.
Working knowledge of CI/CD and DevOps practices applied to data workloads; experience with Databricks observability tools.
Experienced in large-scale production Databricks environments with a strong performance-engineering focus targeting cost and reliability.
Demonstrates platform ownership with skills to influence cross-functional teams and vendors without direct authority.
Familiar with AI-native Databricks capabilities and operational automation, capable of shaping an AI-driven data platform roadmap.