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Popular mid-level data engineer role with broad cloud and tooling requirements increases applicant competition.
Core data engineering skills are transferable, but revenue systems and Fabric-specific experience moderately restrict fit.
Explicit 5+ years plus specific Microsoft Fabric, dbt, and Azure experience creates strict technical filters.
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Build and maintain the Revenue Data Platform integrating data from core revenue systems using Microsoft Fabric and lakehouse architecture.
Design and own dimensional analytics data models supporting reporting, forecasting, and revenue insights across departments.
Implement data quality, observability, governance, and support time-based historical revenue analysis ensuring reliable and trustworthy metrics.
5+ years experience in data engineering, analytics engineering, or data platform roles building production data systems.
Proficiency with Microsoft Fabric or comparable data stack technologies (Databricks, Azure Data Factory), including Azure cloud services.
Strong skills in dimensional data modeling with experience implementing slowly changing dimensions and historical data patterns.
Advanced SQL skills and experience with data transformation tools like dbt or Microsoft Fabric Dataflows.
Experienced in working within modern lakehouse environments and familiar with revenue operations data workflows and metrics.
Capable of collaborating cross-functionally translating business requirements into scalable data models and systems.
Skilled in establishing data governance, testing, monitoring practices, and maintaining high standards for pipeline reliability and observability.