Senior Fabric Data Engineer

Key Responsibilities

• Build, enhance, and maintain data engineering solutions within Microsoft Fabric using established architecture, development standards, and migration patterns.
• Develop and refine ingestion, transformation, and orchestration processes that move data from source systems into curated Fabric data layers.
• Create and regenerate Gold-layer datasets that provide trusted, consumption-ready data for analytics and business use cases.
• Translate business and analytical requirements into scalable data transformations, reusable engineering patterns, and clearly structured data products.
• Develop and optimize Fabric pipelines, notebooks, Lakehouse or Warehouse objects, and supporting transformation logic as required by the solution design.
• Execute historical data movement and backfill activities while preserving completeness, lineage, and consistency across curated data layers.
• Implement data quality controls and engineering validation within pipelines, including row-count and row-level reconciliation where appropriate.
• Investigate data discrepancies and resolve issues in transformation logic, dependencies, schema mappings, orchestration, or source-to-target processing.
• Improve the maintainability, repeatability, and performance of Fabric engineering workflows and data transformations.
• Collaborate with Mastronardi Produce engineers, architects, analysts, and data consumers to align Gold-layer outputs with downstream reporting and analytics needs.
• Document data flows, transformation rules, dependencies, technical decisions, and completed data products.
• Adapt to evolving priorities and contribute to additional Fabric data engineering work as the engagement continues.

Required Experience

• Hands-on Microsoft Fabric data engineering experience in enterprise environments.
• Experience building data pipelines, notebooks, transformations, and curated data products in Microsoft Fabric.
• Experience working with Fabric Lakehouse and/or Warehouse architectures and medallion-style data layers.
• Demonstrated experience designing, building, or regenerating Gold-layer datasets for analytics and downstream consumption.
• Experience with historical data movement, backfills, incremental processing, and source-to-target transformation.
• Strong SQL skills and practical experience implementing data transformation and reconciliation logic.
• Experience diagnosing data quality issues, transformation defects, schema mismatches, and pipeline dependencies.
• Understanding of scalable data modeling, data lineage, orchestration, and maintainable data engineering practices.
• Ability to work within an established engineering approach while improving delivery capacity and throughput.
• Clear communication skills and experience collaborating within co-managed or cross-functional data teams.