Microsoft Fabric Data Engineer

Position Overview :

We are seeking a highly skilled Microsoft Fabric Data Engineer with strong expertise in designing enterprise-grade analytical platforms using Fabric Lakehouse, Data Warehouse, Data Factory (Fabric), and Power BI. The role focuses on building scalable, governed, and high-performance data architectures that support real-time and batch analytics across the organization.

Key Responsibilities :

- Engage with business stakeholders and techno-functional SMEs to translate analytical requirements into Fabric-based architectural designs.

- Design and implement end-to-end analytics solutions using Microsoft Fabric (Lakehouse, Warehouse, OneLake).

- Define data ingestion and integration strategies using Fabric Data Factory pipelines for RDBMS, OData, APIs, and file-based sources.

- Design Lakehouse and Warehouse data models using dimensional and medallion (Bronze/Silver/Gold) patterns.

- Architect scalable storage and compute using OneLake, Direct Lake, and Fabric capacities.

- Implement data governance, security, and access control using Fabric, Purview, and Power BI security models.

- Optimize query performance leveraging Direct Lake, SQL endpoints, and workload management.

- Provide technical leadership and architectural guidance to engineering teams.

- Evaluate emerging Fabric capabilities and recommend adoption aligned to enterprise roadmap.

Qualifications :

- Bachelors degree in Computer Science, Engineering, or related field.

- 2+ years experience designing analytical platforms on Microsoft Fabric or modern Azure analytics stack.

- Strong knowledge of Fabric Lakehouse, Warehouse, Data Factory, Power BI.

- Advanced SQL / T-SQL skills.

- Strong understanding of dimensional modeling and analytics design patterns.

- Experience with data modeling tools (Erwin, Power Designer) good to have.

- Strong communication skills with ability to explain architecture to non-technical stakeholders.

- Microsoft certifications (Fabric, Azure Data Engineer) good to have.