Microsoft Fabric Developer

You will own the design and delivery of data pipelines and analytics models on Microsoft Fabric for a client moving to a unified lakehouse platform. Working onsite, you are the senior technical voice in the room: you shape the solution design, lead hands-on build, mentor junior developers, and be the day-to-day technical contact for business and IT stakeholders. You are expected to work with minimal supervision and hand over production-ready, governed assets. 

Lead design and build of ingestion and transformation pipelines using Fabric Data Factory (pipelines, Dataflows Gen2) and Spark notebooks (PySpark / Spark SQL). 

Define Lakehouse vs Warehouse choices and the medallion (bronze/silver/gold) layering on OneLake with Delta tables, and document the rationale. 

Design and optimize Power BI semantic models, including Direct Lake where appropriate, and guide report developers. 

Lead migration of existing workloads (SSIS, ADF, Synapse, on-prem SQL Server) to Fabric, including assessment, sequencing, and cutover. 

Set up and own CI/CD using Fabric Git integration and deployment pipelines (Azure DevOps or GitHub), including branching strategy and promotion standards. 

Define and enforce security and governance: workspace roles, row/column-level security, sensitivity labels, Purview integration. 

Own performance and capacity management (CU consumption, throttling, SKU sizing inputs), and tune pipelines, Spark jobs, and queries. 

Conduct code and design reviews, set engineering standards, and mentor junior and mid-level developers. 

Run requirement workshops and demos, estimate and break down work, flag risks early, and keep designs and runbooks current. 

8+ years in data engineering, with proven end-to-end delivery on Microsoft Fabric: Lakehouse, Warehouse, Data Factory pipelines, notebooks, OneLake shortcuts, production deployment. 

Expert-level SQL (T-SQL) and PySpark/Python, with deep Delta Lake knowledge (ACID, partitioning, optimize/vacuum, schema evolution, performance tuning). 

Strong dimensional modeling and medallion architecture experience, including making and defending design trade-offs. 

Power BI semantic modeling: DAX, Direct Lake vs Import vs DirectQuery trade-offs, model performance. 

Experience with incremental loads, CDC patterns, and data quality frameworks at scale. 

Git-based version control and CI/CD for data assets, with experience setting it up, not just using it. 

Demonstrated experience leading or mentoring a small team and doing code reviews. 

Strong communication skills for working directly with client stakeholders, including senior business and IT leaders.