Job Description
MICROSOFT FABRIC DEVELOPER
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Duration: 8–12 Months | Type: Remote
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Role Summary
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You will own the design and delivery of data pipelines and analytics models on Microsoft Fabric for a client moving to a
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unified lakehouse platform. You will serve as the senior technical voice across solution design, hands-on development,
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mentoring, and day-to-day technical engagement with business and IT stakeholders. The role requires strong ownership,
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minimal supervision, and delivery of production-ready, governed data assets.
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Responsibilities
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l Lead the design and build of ingestion and transformation pipelines using Fabric Data Factory, including pipelines
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and Dataflows Gen2, as well as Spark notebooks using PySpark and Spark SQL.
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l Define Lakehouse versus Warehouse architecture decisions and establish medallion architecture
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(Bronze/Silver/Gold) on OneLake using Delta tables; document and defend design rationale.
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l Design and optimize Power BI semantic models, including Direct Lake where appropriate, and provide technical
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guidance to report developers.
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l Lead migration of existing workloads such as SSIS, Azure Data Factory, Synapse, and on-premises SQL Server to
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Microsoft Fabric, including assessment, sequencing, and production cutover.
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l Set up and own CI/CD using Fabric Git integration and deployment pipelines with Azure DevOps or GitHub,
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including branching strategy, release controls, and promotion standards.
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l Define and enforce security and governance standards, including workspace roles, row-level and column-level
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security, sensitivity labels, and Microsoft Purview integration.
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l Own performance and capacity management, including CU consumption, throttling, SKU sizing inputs, and tuning of
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pipelines, Spark jobs, and SQL queries.
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l Conduct code and architecture reviews, establish engineering standards, and mentor junior and mid-level data
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engineers.
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l Run requirements workshops and technical demos, estimate and break down work, identify risks early, and maintain
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current architecture documentation and production runbooks.
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Must-Have Qualifications
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• 8+ years of data engineering experience with proven end-to-end production delivery on Microsoft Fabric, including
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Lakehouse, Warehouse, Data Factory pipelines, notebooks, OneLake shortcuts, and production deployment.
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• Expert-level SQL/T-SQL and PySpark/Python, with deep Delta Lake knowledge covering ACID transactions,
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partitioning, OPTIMIZE/VACUUM, schema evolution, and performance tuning.
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• Strong dimensional modeling and medallion architecture experience, with the ability to make and defend architecture
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and design trade-offs.
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• Hands-on Power BI semantic modeling experience, including DAX and Direct Lake versus Import versus
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DirectQuery trade-offs and model-performance optimization.
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• Experience implementing incremental loads, CDC patterns, and data-quality frameworks at scale.
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• Strong Git-based version control and CI/CD experience for data assets, including setting up pipelines and standards
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rather than only consuming an existing process.
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• Demonstrated experience leading or mentoring a small engineering team and performing code and design reviews.
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• Strong communication and stakeholder-management skills, with experience working directly with senior business
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and IT leaders.
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Core Technology Stack
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Microsoft Fabric: Lakehouse, Warehouse, OneLake, Data Factory, Dataflows Gen2, Spark Notebooks, Delta Lake, Git
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Integration, Deployment Pipelines, Capacity Management
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Data Engineering: PySpark, Python, Spark SQL, T-SQL, ETL/ELT, CDC, Incremental Loads, Data Quality
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Analytics: Power BI, DAX, Semantic Models, Direct Lake, Import, DirectQuery
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Migration: SSIS, Azure Data Factory, Azure Synapse, SQL Server
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DevOps & Governance: Git, Azure DevOps/GitHub, CI/CD, Microsoft Purview, RBAC, RLS/CLS, Sensitivity Labels
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Candidate Profile
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The ideal candidate is a senior, hands-on Microsoft Fabric engineer who can operate as both an architect and delivery
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lead. They should be comfortable making platform decisions, writing production code, troubleshooting performance and
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capacity issues, establishing engineering standards, and communicating technical trade-offs directly to client
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stakeholders.