Data Engineer with Microsoft Fabric

You will own the design and delivery of data pipelines and analytics models on Microsoft Fabric for a client moving to a unified lake house 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 One Lake 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, One Lake 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 modelling and medallion architecture experience, including making and defending design trade-offs.
• Power BI semantic modelling: DAX, Direct Lake vs Import vs Direct Query 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.