Senior Data Engineer (Databricks)

experience in CANDIDATES SHOULD HAVE EXPERIENCE WITH: Serving as a Databricks SME ERP integrations (strong plus) Python / PySpark Performance tuning and optimization Databricks maintenance, including VACUUM Data governance and best practices Semantic modeling, Education: Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent practical experience. Track Record: 5+ years dedicated to Databricks/Apache Spark platforms and 8+ years overall in Data Engineering/BI. Proven experience taking an enterprise lakehouse end-to-end into production. System Experience: Deep hands-on experience migrating legacy warehouses (SQL Server, Synapse, ADF) and sourcing data from ERPs (JD Edwards strongly preferred). Certifications (Preferred): Databricks Certified Data Engineer detail oriented/Data Architect; Azure DP-203 or AZ-305. TECHNICAL SKILLS Databricks & Spark: Advanced PySpark, Spark SQL, Databricks Workflows, Delta Live Tables, Unity Catalog, Auto Loader, and Photon. Data Modeling & Storage: Star Schema, SCDs, Conformed Dimensions, Delta Lake (MERGE, Liquid Clustering, Z-ORDER, Time Travel). Cloud & DevOps: Azure Gen2, Key Vault, Entra ID, Git, Azure DevOps/GitHub Actions, Databricks Asset Bundles, and Infrastructure as Code (Terraform). Legacy & Integration: SQL/T-SQL, SSIS, SSRS, Synapse, ADF, CDC/replication tools, Direct Lake/Power BI integration, and Microsoft Fabric/OneLake. AI & Modern Workflow: Agentic AI coding tools, natural language query curation (Databricks Genie), and metadata-driven semantics. SOFT SKILLS Strong technical ownership, architectural influence, and clear communication with both executives and engineers. Excellent cross-functional collaboration with offshore partners, citizen developers, and business stakeholders. Pragmatic problem-solving, critical thinking, and time management across build and legacy support. Technology Doesn’t Change the World, People Do.

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