Job
Description
Job TItle: Microsoft Fabric Data Engineer Location: Bangalore Job Type: Conract (24 Months) Job Description: We are seeking a highly skilled and experienced Microsoft Fabric Data Engineer/Architect to design, develop, and maintain robust, scalable, and secure data solutions within the Microsoft Fabric ecosystem. This role will leverage the full suite of Microsoft Azure data services, including Azure Data Bricks, Azure Data Factory, and Azure Data Lake, to build end-to-end data pipelines, data warehouses, and data lakehouses that enable advanced analytics and business intelligence. Required Skills & Qualifications: Bachelors degree in Computer Science, Engineering, or a related field. 5+ years of experience in data architecture and engineering, with a strong focus on Microsoft Azure data platforms. Proven hands-on expertise with Microsoft Fabric and its components, including: OneLake Data Factory (Pipelines, Dataflows Gen2) Synapse Analytics (Data Warehousing, SQL analytics endpoint) Lakehouses and Warehouses Notebooks (PySpark) Extensive experience with Azure Data Bricks, including Spark development (PySpark, Scala, SQL). Strong proficiency in Azure Data Factory for building and orchestrating ETL/ELT pipelines. Deep understanding and experience with Azure Data Lake Storage Gen2. Proficiency in SQL (T-SQL, Spark SQL), Python, and/or other relevant scripting languages. Solid understanding of data warehousing concepts, dimensional modeling, and data lakehouse architectures. Experience with data governance principles and tools (e.g., Microsoft Purview). Familiarity with CI/CD practices, version control (Git), and DevOps for data pipelines. Excellent problem-solving, analytical, and communication skills. Ability to work independently and collaboratively in a fast-paced, agile environment. Preferred Qualifications: Microsoft certifications in Azure Data Engineering (e.g., DP-203, DP-600: Microsoft Fabric Analytics Engineer Associate). Experience with Power BI for data visualization and reporting. Familiarity with real-time analytics and streaming data processing. Exposure to machine learning workflows and integrating ML models with data solutions
