Role: Data Engineer – Microsoft Fabric Location: India (Bangalore, Hyderabad / Mumbai / Gurugram) Position: Senior Associate Required Experience: 3–6 years Role Summary We’re looking for a skilled Data Engineer with Microsoft Fabric experience to join our growing data and AI team. In this role, you will design and build modern data platforms leveraging Microsoft Fabric, enabling scalable analytics, AI-driven insights, and enterprise-grade data solutions for global clients. This is an excellent opportunity to work on next-generation data architectures, contribute to AI-driven transformation programs, and grow into advanced data engineering and platform leadership roles. Key Responsibilities • Design, build, and maintain scalable data pipelines using Microsoft Fabric Data Factory (Pipelines), Dataflows Gen2, Fabric Notebooks, and Lakehouse. • Develop and manage OneLake/Fabric Lakehouse architectures for structured and semi-structured healthcare and enterprise data. • Build and optimize batch and API-based ingestion pipelines from multiple enterprise data sources, databases, files, and application systems. • Develop robust ETL/ELT workflows using SQL, Python, and PySpark for data transformation and enrichment. • Implement data quality, validation, reconciliation, completeness, consistency, and anomaly-detection checks across source and target datasets. • Develop data standardization, deduplication, entity-resolution, record-linkage, and matching workflows across heterogeneous data sources. • Design analytics-ready datasets and data models for data scientists, analysts, ML pipelines, and downstream applications. • Collaborate closely with data scientists and ML engineers to prepare reliable feature-ready and model-consumable datasets. • Integrate Microsoft Fabric with Azure data services such as Azure Data Lake Storage, Azure SQL, Synapse components, and Power BI where required. • Implement incremental ingestion, change-data handling, error handling, retry mechanisms, and pipeline recovery strategies where applicable. • Implement observability and monitoring using Azure Log Analytics, alerts, action groups, and pipeline-level monitoring. • Support data lineage, metadata management, governance, security, and discoverability using Fabric Catalog and/or Microsoft Purview. • Optimize SQL queries, Spark transformations, storage strategies, and pipeline execution for large-scale datasets. • Support CI/CD, Git-based version control, deployment automation, and DataOps practices for Fabric data solutions. Required Qualifications • Bachelor’s/Master’s degree in Computer Science, Engineering, Data Engineering, or a related field, or equivalent practical experience. • 3–6 years of hands-on data engineering experience, including practical Microsoft Fabric experience. • Strong hands-on experience with Microsoft Fabric Lakehouse, OneLake, Data Factory/Pipelines, Dataflows Gen2, and Fabric Notebooks. • Strong SQL skills, including complex queries, data transformation, optimization, and data modeling. • Strong Python and/or PySpark experience for data transformation and pipeline development. • Experience designing and implementing ETL/ELT pipelines and integrating data from APIs, databases, files, and enterprise systems. • Experience implementing data quality, validation, reconciliation, and error-handling workflows. • Experience with data matching, entity resolution, deduplication, record linkage, or similar data integration workflows. • Working knowledge of Azure Data Lake Storage, Azure SQL, and/or Synapse Analytics. • Understanding of data warehousing concepts, dimensional modeling, and analytics-ready data design. • Familiarity with Git, CI/CD, deployment automation, and DataOps practices. • Understanding of data governance, security, lineage, metadata, and performance optimization. Preferred Qualifications • Experience working with healthcare/provider data. • Experience integrating Power BI with enterprise data platforms. • Exposure to Microsoft Purview or Fabric Catalog. • Experience with incremental/CDC ingestion and event-driven or near-real-time data pipelines. • Exposure to Eventstream, Azure Event Hubs, or other streaming ingestion frameworks.