Lead Databricks lakehouse execution across bronze, silver, and gold with a focus on maintainable Lakeflow-driven pipelines and high-quality curated outputs.
Own common data model implementation, conformed dimensions, fact design, and reusable gold datasets for reporting, forecasting, and AI/BI consumption.
Design and build scalable data models for SAP, manufacturing, finance, sales, supply chain, and operational domains.
Partner on source onboarding and ensure new data is shaped into governed medallion outputs that are usable for analytics and AI.
Implement data quality checks, reconciliation logic, documentation, and performance optimization across structured data assets.
Provide technical leadership for engineering backlog execution, code quality, testing discipline, and repeatable delivery standards in Databricks.

7+ years of experience in data engineering with strong ownership of Databricks, data modeling, and production data pipelines.
Deep experience with Spark, SQL, Python, medallion architecture, and dimensional or conformed data model design.
Experience working with SAP or comparable ERP data and complex manufacturing, finance, supply chain, or commercial subject areas.
Experience building governed curated datasets for BI, forecasting, and AI enablement.
Strong understanding of ETL/ELT patterns, source-to-target mapping, data quality, and performance optimization.
Experience with Lakeflow, Fivetran-fed source patterns, Unity Catalog governance, and Power BI semantic consumption, preferred.
Experience supporting AI/BI readiness, trusted metrics, and curated outputs for natural-language analytics, preferred.
Exposure to observability, CI/CD, and testing frameworks in Databricks, preferred.

Senior Data Engineer

Key skills