The role involves designing and building data pipelines and transformations within Microsoft Fabric, including Semantic Models, Dataflows, Lakehouse, and Direct Lake
Job Summary
The role involves designing and building data pipelines and transformations within Microsoft Fabric, including Semantic Models, Dataflows, Lakehouse, and Direct Lake.
The candidate will optimize Lakehouse performance through efficient table design, file size management, and query tuning across multiple engines.
Collaboration with Power BI engineers is essential to deliver trusted, high-performing datasets supporting investment, risk, and operations teams.
Matching Summary
The role involves designing and building data pipelines and transformations within Microsoft Fabric, including Semantic Models, Dataflows, Lakehouse, and Direct Lake.
Skills & Requirements
Must-have
Microsoft Fabric technology suite
Power BI data modeling
Data pipeline design and support
Lakehouse performance tuning
Python and PySpark for data transformation
SQL performance tuning
Medallion architecture implementation
Nice-to-have
Cloud-based data environments (Azure)
Data connectors familiarity
Metadata management and data governance
CI/CD pipelines and source control
Power BI best practices alignment
Automation for scheduling and orchestration
Data quality and schema validation
Key Requirements
3+ years experience with Microsoft Fabric and Power BI
Advanced language proficiency 80-95%
Experience with Python and/or PySpark
Proficiency in SQL with large dataset tuning
Experience in medallion architecture
Experience in cloud data environments, preferably Azure