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Senior

Power BI Developer (7?10 Years) ? C4 Summary We are looking for an experienced Senior Power BI Developer with 7?10 years of expertise in designing and delivering enterprise-scale Business Intelligence solutions. The ideal candidate should possess deep expertise in Power BI, Data Modelling, DAX, and performance optimization, complemented by strong SQL skills and hands-on experience with Microsoft Fabric, Azure Data Factory (ADF), and PySpark.

The role requires the ability to design scalable semantic models, optimize reporting solutions, and collaborate with business and engineering teams to deliver high-quality analytics platforms.

Primary Skills

Power BI & Semantic Modelling

Design and develop enterprise-grade Power BI dashboards, reports, and semantic models.

Expert knowledge of dimensional modelling, Star Schema, Snowflake Schema, and Kimball modelling principles.

Develop complex DAX measures, calculation groups, field parameters, and reusable business logic.

Build efficient Power Query (M) transformations and optimize data refresh strategies.

Implement Row-Level Security (RLS), Object-Level Security (OLS), and governance best practices.

Optimize report performance using Performance Analyzer, DAX Studio, VertiPaq Analyzer, and best practices for semantic model optimization.

Experience with Power BI Service, Deployment Pipelines, Dataflows, Gateways, and Workspace administration.

SQL

Strong expertise in SQL development, including stored procedures, views, CTEs, window functions, query optimization, and indexing.

Experience performing data validation, reconciliation, and root cause analysis.

Strong understanding of relational databases and database performance tuning.

Secondary Skills Microsoft

Fabric

Hands-on experience with Microsoft Fabric including Lakehouse, Data Warehouse, OneLake, Data Factory, Semantic Models, and Real-Time Intelligence.

Experience implementing end-to-end analytics solutions within the Fabric ecosystem.

Knowledge of CI/CD, Git integration, deployment pipelines,



and workspace governance.

Azure Data Factory (ADF)

Design, develop, and monitor scalable ETL/ELT pipelines.

Experience integrating data from cloud, on-premises, APIs, and relational databases.

Strong understanding of pipeline orchestration, parameterization, and monitoring.

PySpark

Solid hands-on experience developing PySpark-based ETL pipelines.

Perform large-scale data transformation, cleansing, and aggregation using DataFrames.

Familiarity with Spark optimization techniques, partitioning, caching, and performance tuning.

Key Responsibilities

Design scalable and high-performing BI solutions aligned with business requirements.

Develop optimized semantic models and reusable DAX calculations.

Lead report performance tuning and data model optimization initiatives.

Collaborate with data engineering teams to design efficient data pipelines.

Review code, establish development standards, and mentor junior developers.

Participate in solution design, estimation, technical reviews, and architecture discussions.

Ensure adherence to data governance, security, and BI best practices.

Support production deployments, troubleshooting, and continuous improvement initiatives.

Required Qualifications & Skills

7?10 years of experience in Business Intelligence and Analytics.

Expert-level proficiency in Power BI, Data Modelling, DAX, and report optimization.

Strong command of SQL with proven experience in query and database performance tuning.

Hands-on experience with Microsoft Fabric, Azure Data Factory, and PySpark.

Experience with Azure DevOps, Git, CI/CD, and Agile delivery methodologies.

Excellent analytical, communication, stakeholder management, and problem-solving skills.

Ability to lead technical discussions, mentor team members, and drive best practices across BI development.

Preferred Certifications

Microsoft Certified: Power BI Data Analyst Associate (PL-300) OR Microsoft Certified: Fabric Analytics Engineer Associate (DP-600)

Microsoft Certified: Azure Data Engineer Associate (DP-203) ? Preferred

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