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Metro-based, mid-level Data Engineer title and 5+ years make it highly competitive.
Core data engineering skills transfer across industries, though Fabric and insurance experience moderately narrow fit.
Explicit 5+ years plus mandatory Microsoft Fabric, PySpark, Azure, and Purview skills increase shortlisting strictness.
Develop and maintain Microsoft Fabric / OneLake Lakehouse solutions spanning Bronze, Silver, and Gold layers with data ingestion, transformation, and curated datasets.
Build and optimize scalable Spark / PySpark, Python, and SQL data pipelines integrating sources like GCP, CSV, Excel, and SharePoint with CDC and Medallion Architecture.
Implement data quality frameworks, operational monitoring, and governance controls including Purview, RBAC, and PII/PHI compliance while collaborating with architects and BI teams.
5+ years of data engineering experience with hands-on expertise in Azure / Microsoft Fabric technologies.
Proficiency in Spark / PySpark, Python, SQL, Microsoft Fabric Lakehouse, OneLake, Data Pipelines, Notebooks, Delta Lake, and ETL/ELT including CDC frameworks.
Experience with data quality, validation, reconciliation, exception handling, and familiarity with Power BI semantic models and Direct Lake.
Knowledge of Microsoft Purview, Azure DevOps, Git, CI/CD deployment processes; commercial insurance or brokerage data experience preferred but not mandatory.
Experienced data engineer skilled in building multi-layered lakehouse data solutions and reusable ingestion pipelines in a Microsoft Fabric environment.
Comfortable troubleshooting production data pipelines, implementing data governance controls, and collaborating cross-functionally with solution architects and business stakeholders.
Has practical domain experience in commercial insurance or brokerage data to add contextual knowledge, enhancing solution relevance and impact.