Data Analytics Engineer (Microsoft Fabric & Power BI)

Job Description Senior Microsoft Fabric Engineer / Fabric Technical Lead (6+ Years)

Job Title: Senior Microsoft Fabric Engineer / Fabric Technical Lead

Job Summary

We are looking for an experienced Microsoft Fabric Engineer to design, architect, and lead enterprise analytics solutions using Microsoft Fabric. The candidate will be responsible for implementing scalable Lakehouse architectures, Data Engineering workloads, Real-Time Intelligence, enterprise Power BI solutions, governance, and performance optimization while mentoring junior engineers.

Key Responsibilities: Senior Microsoft Fabric Engineer (6+ Years)

Design and implement enterprise-scale Microsoft Fabric solutions.

Architect scalable Lakehouse, Warehouse, and One Lake environments following Medallion Architecture (Bronze, Silver, Gold).

Design, develop, and optimize Fabric Notebooks using Python, PySpark, and Spark SQL.

Lead the development of reusable Python frameworks and notebook-based ETL/ELT solutions.

Optimize Spark jobs, notebook execution, and distributed data processing performance.

Build and maintain Data Factory Pipelines, Dataflows Gen2, and orchestration workflows.

Develop enterprise semantic models and Power BI reporting solutions.

Integrate data from SQL Server, Oracle, SAP, Azure Data Lake, REST APIs, Databricks, and other enterprise platforms.

Implement CI/CD, Git integration, deployment pipelines, and environment management.

Troubleshoot complex production issues involving notebooks, pipelines, Spark clusters, and Power BI semantic models.

Mentor junior engineers on Fabric development, Python coding standards, and best practices.

Collaborate with solution architects, business stakeholders, and cross-functional teams to deliver enterprise analytics solutions.

Prepare technical documentation, architecture diagrams, and operational runbooks.

Data Engineering

Develop scalable ETL/ELT pipelines.

Build PySpark notebooks.

Optimize Spark jobs.

Implement incremental processing.

Develop reusable data engineering frameworks.

Design dimensional data models.

Data Integration

Build Data Factory Pipelines.

Develop Dataflows Gen2.

Integrate data from: .