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Fabric Data Engineers build the pipelines that move data from source systems into lakehouse tables and warehouse endpoints. They write PySpark code in Fabric notebooks, design Data Factory v2 pipelines that orchestrate multi-step ingestion workflows, and implement the medallion architecture patterns that transform raw data into business-ready datasets.

The role sits between the architect (who designs the platform) and the analyst (who consumes the data). Data engineers handle the unglamorous but critical work: schema evolution strategies for Delta tables, incremental load patterns that avoid full refreshes, error handling in pipelines that process millions of rows daily, and performance tuning of Spark jobs that run against terabytes of lakehouse data.

A strong Fabric Data Engineer understands the nuances of the Fabric runtime — how Spark pools are managed differently than in Synapse, how Data Factory v2 pipelines differ from their predecessor, and how to use shortcuts to reference data across lakehouses without physical duplication. They also understand the cost implications of their engineering choices, because a poorly optimized Spark notebook can burn through capacity units in ways that don't surface until the monthly Azure bill arrives.

We identify Fabric Data Engineers who have built production pipelines — not just completed tutorials. Our evaluation focuses on the engineering decisions they've made: how they handle slowly changing dimensions in Delta, what their approach is to pipeline failure recovery, and whether they've worked with Data Factory v2's specific orchestration model. We also verify their PySpark fluency, because Fabric's notebook experience rewards engineers who can write efficient distributed code rather than relying solely on low-code dataflows.