Power BI Semantic Modeling & Azure Databricks - Engineer

Project overview

This contract role will build and optimize an Azure Databricks lakehouse and the Power BI semantic layer that supports enterprise and self-service analytics. The core requirements are hands-on PySpark, Databricks SQL, Unity Catalog and Delta Lake, strong SQL and Kimball dimensional modeling, and Power BI semantic modeling with DAX and storage-mode knowledge. Experience with Tabular Editor, ADLS Gen2, Azure Data Factory, and Entra ID authentication is also specified. The description lists 4+ years of relevant experience, while the structured input gives a 4–8-year band.

The role is based in Mumbai; work mode is not specified, and the engagement is contract, with DLT, XMLA, ALM, capacity management, and CI/CD tooling as additional relevant experience.

Responsibilities

• Data Modeling & Lakehouse Architecture
• Design, build, and maintain production-grade Star Schema and Snowflake data models within Azure Databricks (Unity Catalog).
• Optimize Delta Lake tables using OPTIMIZE, Z-ORDERING, liquid clustering, partition strategies, and dynamic file sizing for high-speed downstream analytics.
• Power BI Semantic Layer Engineering
• Build, publish, and govern enterprise-scale Power BI Semantic Models (formerly Datasets) leveraging Direct Lake, Import, and DirectQuery storage modes.
• Write clean, optimized DAX measures and enforce star-schema relationships while keeping complex logic upstream in Databricks.
• Manage XMLA endpoints, deployment pipelines, ALM Toolkit, and Power BI Premium / Fabric capacity utilization.
• Pipeline & Compute Engineering
• Build robust ETL/ELT pipelines using PySpark, Databricks SQL, Delta Live Tables (DLT), and Azure Data Factory (ADF).
• Configure and fine-tune Serverless and Pro Databricks SQL Warehouses to handle concurrent reporting workloads cost-effectively.
• Governance, Security & Operations
• Implement Row-Level Security (RLS) and Column-Level Security (CLS) across Unity Catalog and Power BI semantic models.

Requirements

• PySpark
• Databricks SQL
• Unity Catalog
• Delta Lake performance tuning, including Z-Ordering and liquid clustering
• Power BI semantic model design and storage modes (Direct Lake, DirectQuery, Import)
• DAX authoring and performance optimization
• Tabular Editor
• Azure Data Lake Storage Gen2 (ADLS Gen2)
• Azure Data Factory (ADF)
• Entra ID authentication
• Expert-level SQL
• Kimball dimensional modeling, including facts, dimensions, and slowly changing dimensions

Project details

• Start: flexible
• Remote: onsite
• Location: India, IN