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Senior niche Databricks role reduces pool, but remote and Bangalore location increase candidate density.
Highly domain-specific Databricks and lakehouse expertise limits cross-industry transferability.
Explicit 12+ years, mandatory Databricks expertise, and leadership responsibilities make filters highly strict.
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Own the architecture, technical vision, and multi-year roadmap for the Databricks Lakehouse platform including Unity Catalog, Delta Lake, and Spark.
Lead design and scaling of data pipelines and AI-ready datasets supporting GenAI, ML, and BI use cases with medallion (Bronze/Silver/Gold) lakehouse architecture.
Drive platform standards for data quality, security, access control, CI/CD, incident management, and mentor senior engineers while leading migrations from legacy systems to Databricks.
12+ years in data engineering with at least 3+ years hands-on with Databricks in production.
Deep expertise in Unity Catalog, Delta Lake, Spark performance tuning, strong SQL and Python skills.
Experience with at least one major cloud platform (AWS, Azure, or GCP) and infrastructure-as-code tools.
Work Experience Required: 12+ years in data engineering; Notice Period: Not explicitly mentioned in the JD.
Experienced architect-level data engineer with proven leadership in designing and operationalizing large-scale Databricks Lakehouse platforms.
Strong strategic focus on AI/ML-ready data infrastructure and setting/adopting engineering standards across teams.
Track record of mentoring, cross-functional collaboration, and driving adoption of agent-based AI tooling and CI/CD for data pipelines.