Fabric Manager

MICROSOFT FABRIC DEVELOPER

Duration: 8–12 Months | Type: Remote

Role Summary

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. You will serve as the senior technical voice across solution design, hands-on development,

mentoring, and day-to-day technical engagement with business and IT stakeholders. The role requires strong ownership,

minimal supervision, and delivery of production-ready, governed data assets.

Responsibilities

l Lead the design and build of ingestion and transformation pipelines using Fabric Data Factory, including pipelines

and Dataflows Gen2, as well as Spark notebooks using PySpark and Spark SQL.

l Define Lakehouse versus Warehouse architecture decisions and establish medallion architecture

(Bronze/Silver/Gold) on OneLake using Delta tables; document and defend design rationale.

l Design and optimize Power BI semantic models, including Direct Lake where appropriate, and provide technical

guidance to report developers.

l Lead migration of existing workloads such as SSIS, Azure Data Factory, Synapse, and on-premises SQL Server to

Microsoft Fabric, including assessment, sequencing, and production cutover.

l Set up and own CI/CD using Fabric Git integration and deployment pipelines with Azure DevOps or GitHub,

including branching strategy, release controls, and promotion standards.

l Define and enforce security and governance standards, including workspace roles, row-level and column-level

security, sensitivity labels, and Microsoft Purview integration.

l Own performance and capacity management, including CU consumption, throttling, SKU sizing inputs, and tuning of

pipelines, Spark jobs, and SQL queries.

l Conduct code and architecture reviews, establish engineering standards, and mentor junior and mid-level data

engineers.

l Run requirements workshops and technical demos, estimate and break down work, identify risks early, and maintain

current architecture documentation and production runbooks.

Must-Have Qualifications

• 8+ years of data engineering experience with proven end-to-end production delivery on Microsoft Fabric, including

Lakehouse, Warehouse, Data Factory pipelines, notebooks, OneLake shortcuts, and production deployment.

• Expert-level SQL/T-SQL and PySpark/Python, with deep Delta Lake knowledge covering ACID transactions,

partitioning, OPTIMIZE/VACUUM, schema evolution, and performance tuning.

• Strong dimensional modeling and medallion architecture experience, with the ability to make and defend architecture

and design trade-offs.

• Hands-on Power BI semantic modeling experience, including DAX and Direct Lake versus Import versus

DirectQuery trade-offs and model-performance optimization.

• Experience implementing incremental loads, CDC patterns, and data-quality frameworks at scale.

• Strong Git-based version control and CI/CD experience for data assets, including setting up pipelines and standards

rather than only consuming an existing process.

• Demonstrated experience leading or mentoring a small engineering team and performing code and design reviews.

• Strong communication and stakeholder-management skills, with experience working directly with senior business

and IT leaders.

Core Technology Stack

Microsoft Fabric: Lakehouse, Warehouse, OneLake, Data Factory, Dataflows Gen2, Spark Notebooks, Delta Lake, Git

Integration, Deployment Pipelines, Capacity Management

Data Engineering: PySpark, Python, Spark SQL, T-SQL, ETL/ELT, CDC, Incremental Loads, Data Quality

Analytics: Power BI, DAX, Semantic Models, Direct Lake, Import, DirectQuery

Migration: SSIS, Azure Data Factory, Azure Synapse, SQL Server

DevOps & Governance: Git, Azure DevOps/GitHub, CI/CD, Microsoft Purview, RBAC, RLS/CLS, Sensitivity Labels

Candidate Profile

The ideal candidate is a senior, hands-on Microsoft Fabric engineer who can operate as both an architect and delivery

lead. They should be comfortable making platform decisions, writing production code, troubleshooting performance and

capacity issues, establishing engineering standards, and communicating technical trade-offs directly to client

stakeholders.