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The ideal candidate will have expertise in end-to-end data engineering, particularly API-based data ingestion frameworks in Fabric and advanced Databricks capabilities.\n\nKey Responsibilities : \n\n- Design, develop, and maintain end-to-end data engineering solutions using Microsoft Fabric and Databricks.\n\n- Build scalable API-based data ingestion frameworks in Microsoft Fabric.\n\n- Develop and optimize data pipelines for data ingestion, transformation, and processing.\n\n- Implement Databricks solutions using Delta Live Tables (DLT), Auto Loader, and Unity Catalog.\n\n- Ensure data quality, security, governance, and pipeline reliability.\n\n- Optimize data processing workflows and troubleshoot performance issues.\n\nRequired Skills : \n\n- 5 - 8 years of relevant data engineering experience.\n\n- Strong expertise in end-to-end Microsoft Fabric Data Engineering.\n\n- Expert-level experience in API-based data ingestion frameworks within Fabric.\n\n- Hands-on experience with Databricks, Delta Live Tables (DLT), Auto Loader, and Unity Catalog.\n\n- Proficiency in SQL and Python/PySpark.\n\n- Strong understanding of ETL/ELT, data transformation, and pipeline orchestration.\n\n- Experience with Lakehouse architecture, Delta Lake, and data quality management.\n\n- Knowledge of API integration, authentication, pagination, and error handling.\n\nPreferred Qualifications : \n\n- Experience integrating Microsoft Fabric with Databricks.\n\n- Familiarity with Azure Data Lake Storage Gen2 and Azure services.\n\n- Knowledge of CI/CD, Git, medallion architecture, data governance, and lineage.\n\n- Strong analytical, problem-solving, and communication skills.\n\nJob Details : \n\nExperience : 5 - 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