Sr. Data Engineer/BI Modeler

Must Haves:

• Strong enterprise Power BI semantic modeling experience
• Strong dimensional data modeling skills, including star schemas, fact/dimension design, conformed dimensions, relationships, cardinality, and appropriate data grain
• Strong SQL skills for data transformation, analysis, validation, and data product development
• Strong hands-on experience with Azure Synapse and/or Microsoft Fabric, ideally exposure to both (can flex on no Fabric experience)
• Hands-on data engineering experience across data ingestion, transformation, pipelines, and curated analytical datasets
• Experience designing and building Lakehouse and/or Warehouse structures supporting analytics and BI
• Strong DAX and Power Query experience
• Understanding of data quality, profiling, cleansing, validation, and reconciliation
• Ability to translate business requirements into scalable technical data solutions
• Strong communication skills and ability to work directly with business stakeholders, Data Engineers, Architects, Analysts, and BI teams

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Nice to Haves:

• Hands-on Microsoft Fabric experience, particularly Fabric Data Factory/pipelines, Lakehouse, Warehouse, and notebooks
• Experience migrating or modernizing Azure Synapse workloads into Microsoft Fabric
• Python and/or PySpark
• Delta/Parquet experience
• CI/CD and source control experience for data/analytics solutions
• Power BI performance tuning and optimization
• Experience with Power BI/Power Platform Centers of Excellence
• Financial services, automotive finance, lending, claims, or related financial data experience
• Experience building Power BI reports/dashboards when needed

Team / Project Overview:

The team supports both internal and external data and analytics needs across areas including Sales, Finance, Claims, external data exchange, and dealer-facing reporting. The team currently operates across three pods and is evolving its data platform from Azure Synapse toward Microsoft Fabric.

This role sits between traditional BI modeling and Data Engineering. The primary focus is building governed, reusable data products and enterprise semantic models, but the individual will also be expected to work hands-on across the data lifecycle, including ingestion, transformation, pipelines, Lakehouse/Warehouse structures, and data quality. The expectation is approximately 40–50% hands-on data engineering, with the remaining focus primarily on semantic modeling, BI, and data product development. xhyhwjd

The ideal candidate has successfully worked across both sides of the stack rather than being exclusively a Power BI developer or exclusively a Data Engineer.

Responsibilities:

• Design, develop, and optimize enterprise semantic models and governed data products supporting reporting, analytics, and self-service use cases
• Partner with business stakeholders to understand requirements and translate them into scalable data and analytics solutions
• Design dimensional models using appropriate facts, dimensions, relationships, granularity, and reusable business logic
• Build and optimize Power BI semantic models, DAX measures, and supporting data structures
• Perform hands-on data engineering, including ingestion, transformation, pipelines, notebooks, and preparation of data for analytical consumption
• Create curated Lakehouse/Warehouse structures and reusable datasets supporting semantic models and downstream consumers
• Profile, cleanse, aggregate, normalize, and validate data to ensure trusted and accurate data products
• Implement data-quality checks and troubleshoot data anomalies and performance issues
• Support the organization's transition from Azure Synapse toward Microsoft Fabric
• Participate in deployment, source control, CI/CD, monitoring, and ongoing support of analytics data products
• Build or support Power BI reports and dashboards as needed; dashboard development is not the primary focus of the role
• Collaborate across Data Engineering, Architecture, Analytics, BI, and business teams in an Agile delivery environment