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Mid-level data engineer, metro posting and common title create high competition despite Fabric specialization.
Core data engineering transferable, but Fabric tooling and insurance-data preference increase domain specificity.
Explicit 5+ years, Microsoft Fabric, Spark, Purview, and Azure toolchain make filters strict.
Develop and maintain Microsoft Fabric / OneLake Lakehouse solutions covering Bronze, Silver, and Gold data layers with full lifecycle responsibility.
Build and optimize scalable data ingestion pipelines, CDC frameworks, and transformation processes using Spark/PySpark, Python, SQL, and Fabric Data Pipelines.
Implement and uphold data quality, governance, security standards and operational controls while collaborating with solution architects and stakeholders to deliver scalable data solutions.
Minimum 5+ years of Data Engineering experience with practical knowledge in Azure / Microsoft Fabric ecosystem.
Hands-on expertise with Microsoft Fabric Lakehouse, OneLake, Data Pipelines, Notebooks, Spark/PySpark, Python, SQL, and Delta Lake.
Proven experience in data ingestion (ETL/ELT), CDC, Medallion Architecture, data quality, validation, reconciliation, and exception-handling frameworks.
Working knowledge of Power BI semantic models, Microsoft Purview, Azure DevOps, Git, CI/CD, and deployment processes.
Strong background in building end-to-end data lakehouse solutions within Azure/Microsoft Fabric environments.
Experience working with complex, multi-layered data architectures (Bronze, Silver, Gold) and applying Medallion Architecture principles.
Familiarity with commercial insurance or brokerage data domain is a preferred advantage but not mandatory.