Azure Data Engineer with Microsoft Fabric

Job description

**Key Responsibilities:** - Design and develop end-to-end data engineering solutions using **Microsoft Fabric** (Dataflows Gen2, Pipelines, Lakehouse, OneLake). - Build and manage **data ingestion pipelines** using Azure services such as **ADLS Gen2, Azure Data Factory, Azure Synapse, Event Hubs**. - Develop **Notebooks** using PySpark/Spark SQL for data transformation and validation. - Implement and optimize **Lakehouse architectures** using Delta/Parquet formats. - Create and maintain **Semantic Models** in Fabric for reporting and BI consumption. - Implement secure access using **RBAC, Key Vault, Managed Identity**, and follow best practices for PII protection. - Improve performance through partitioning, indexing, and workload tuning within Fabric. - Build CI/CD pipelines using **Git & Azure DevOps** for data engineering workflows. - Collaborate with business, analytics, and architecture teams to deliver scalable data solutions. **Required Skills & Experience:** - Experience in **Data Engineering**. - Strong hands-on experience with **Microsoft Fabric** (mandatory). - Proficiency in **Azure Data Services**: ADLS Gen2, ADF/Synapse, Azure SQL. - Expertise in **SQL**, **PySpark**, **Spark SQL**, and Delta Lake. - Experience with **Pipelines, Dataflows Gen2, Lakehouse, Notebooks** in Fabric. - Understanding of **data modeling**, **data warehousing**, and **semantic layer design**. - Working knowledge of **DevOps practices**, Git, CI/CD pipelines. - Experience in **data governance**, Purview, lineage, and metadata management. - Strong understanding of **security**: RBAC, Key Vault, encryption, PII handling. **Good to Have Skills:** - Certifications: **DP203 (Azure Data Engineer)** or **DP600 (Fabric Analytics Engineer)**. - Experience with **Power BI** (DAX, RLS, semantic model optimization). - BFSI/Financial domain experience. - Experience with Databricks or Synapse Spark environments.