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The candidate should be comfortable working in fast-paced environments, troubleshooting complex data engineering problems, and driving optimization initiatives.\n</p>\n<p>\n <strong>\n  Key Responsibilities:\n </strong>\n</p>\n<ul>\n <li>\n  Design, develop, and maintain scalable and high-performance data pipelines using Databricks and PySpark.\n </li>\n <li>\n  Build and manage ingestion frameworks for batch and streaming data workloads.\n </li>\n <li>\n  Develop enterprise-grade data solutions using Delta Lake and Medallion Architecture principles.\n </li>\n <li>\n  Implement and manage data pipelines using DLT (Delta Live Tables) and Spark Streaming.\n </li>\n <li>\n  Work extensively with Unity Catalog (UC) for governance and data access management.\n </li>\n <li>\n  Optimize Spark jobs, SQL queries, and distributed processing workloads for performance and cost efficiency.\n </li>\n <li>\n  Participate in CI/CD and release management processes using Git and Databricks Asset Bundles.\n </li>\n <li>\n  Collaborate with business stakeholders, architects, and client teams to understand requirements and deliver robust solutions.\n </li>\n <li>\n  Troubleshoot production issues, identify root causes, and implement sustainable fixes.\n </li>\n <li>\n  Drive engineering best practices, code quality standards, and reusable framework development.\n </li>\n <li>\n  Proactively communicate risks, blockers, and improvement opportunities.\n </li>\n <li>\n  Challenge existing processes and contribute ideas to improve platform scalability, reliability, and engineering maturity.\n </li>\n</ul>\n<p>\n <strong>\n  Must-Have Skills &amp; 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