Microsoft Fabric Analytics Engineer

Role Overview: As a candidate for this position, you will be responsible for setting up and configuring end-to-end POC in Microsoft Fabric, including Lakehouse, Warehouse, Data Factory, Notebooks, and Semantic Model. You will also implement Medallion architecture (Bronze/Silver/Gold) using Delta tables in OneLake, build ingestion pipelines from various sources, develop PySpark notebooks, design star schema in Gold layer, and enable governance aspects like lineage, workspace roles, sensitivity labels, and data catalog visibility. Additionally, you will be expected to benchmark performance metrics, document architecture and design choices, and compare with legacy stack. Key Responsibilities: - Set up and configure end-to-end POC in Microsoft Fabric, including various components like Lakehouse, Warehouse, Data Factory, Notebooks, and Semantic Model. - Implement Medallion architecture (Bronze/Silver/Gold) using Delta tables in OneLake. - Build ingestion pipelines from CSV/Excel/REST/SQL using Data Factory Gen2 and Dataflows Gen2. - Develop PySpark notebooks for data cleansing, transformation, and aggregation purposes. - Design star schema in Gold layer and publish Semantic Model for reporting. - Enable governance features such as lineage, workspace roles, sensitivity labels, and data catalog visibility. - Benchmark performance metrics like load time, query latency, refresh duration, and observe capacity/cost behavior. - Document architecture, design choices, limitations, and comparison with the legacy stack. Qualification Required: - Hands-on experience with Microsoft Fabric components like Lakehouse, Warehouse (SQL endpoint), Data Factory, and Notebooks. - Strong proficiency in PySpark for ELT transformations on Delta Lake. - Familiarity with data modeling concepts such as Star schema and Semantic Model design. - Proficiency in Power BI, including Advanced DAX, report optimization, and RLS. - Advanced SQL skills for analytics workloads. - Understanding of Medallion architecture and Lakehouse patterns. - Knowledge of governance aspects like lineage, access control, and sensitivity labeling. Additional Details: The company emphasizes the importance of hands-on experience with Microsoft Fabric components, strong PySpark skills, data modeling proficiency, and knowledge of governance and performance benchmarking. Good-to-have skills include Git integration, REST API ingestion patterns, performance tuning strategies, security design patterns, and comparison knowledge between Fabric and Synapse + ADLS + Power BI architectures. Role Overview: As a candidate for this position, you will be responsible for setting up and configuring end-to-end POC in Microsoft Fabric, including Lakehouse, Warehouse, Data Factory, Notebooks, and Semantic Model. You will also implement Medallion architecture (Bronze/Silver/Gold) using Delta tables in OneLake, build ingestion pipelines from various sources, develop PySpark notebooks, design star schema in Gold layer, and enable governance aspects like lineage, workspace roles, sensitivity labels, and data catalog visibility. Additionally, you will be expected to benchmark performance metrics, document architecture and design choices, and compare with legacy stack. Key Responsibilities: - Set up and configure end-to-end POC in Microsoft Fabric, including various components like Lakehouse, Warehouse, Data Factory, Notebooks, and Semantic Model. - Implement Medallion architecture (Bronze/Silver/Gold) using Delta tables in OneLake. - Build ingestion pipelines from CSV/Excel/REST/SQL using Data Factory Gen2 and Dataflows Gen2. - Develop PySpark notebooks for data cleansing, transformation, and aggregation purposes. - Design star schema in Gold layer and publish Semantic Model for reporting. - Enable governance features such as lineage, workspace roles, sensitivity labels, and data catalog visibility. - Benchmark performance metrics like load time, query latency, refresh duration, and observe capacity/cost behavior. - Document architecture, design choices, limitations, and comparison with the legacy stack. Qualification Required: - Hands-on experience with Microsoft Fabric components like Lakehouse, Warehouse (SQL endpoint), Data Factory, and Notebooks. - Strong proficiency in PySpark for ELT transformations on Delta Lake. - Familiarity with data modeling concepts such as Star schema and Semantic Model design. - Proficiency in Power BI, including Advanced DAX, report optimization, and RLS. - Advanced SQL skills for analytics workloads. - Understanding of Medallion architecture and Lakehouse patterns. - Knowledge of governance aspects like lineage, access control, and sensitivity labeling. Additional Details: The company emphasizes the importance of hands-on experience with Microsoft Fabric components, strong PySpark skills, data modeling proficiency, and knowledge of governance and performance benchmarking. Good-to-have skills include Git integration, REST API ingestion patterns, performanc