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In this role, you will be a core contributor to our SQL Server backend, writing complex stored procedures, views, and aggregation logic that feeds downstream reporting and analytical workloads. Alongside traditional SQL Server development, you will extend these capabilities into the modern lakehouse using Python and Databricks, enabling scalable batch and near-real-time data processing across large datasets. The ideal candidate brings deep expertise in SQL Server (T-SQL, stored procedures, query tuning, indexing strategies) combined with hands-on experience in PySpark, Apache Iceberg, and Databricks Workflows - and is comfortable bridging the gap between on-premise SQL environments and cloud-based data platforms.\nResponsibilities\nDesign and implement robust ETL/ELT pipelines using PySpark and Python on Databricks\nBuild and manage Apache Iceberg table formats for efficient data storage, versioning, and time-travel queries on the data lakehouse\nDevelop and optimize complex SQL queries, stored procedures, and data models for analytical workloads\nAutomate infrastructure provisioning, deployment, and operational tasks using PowerShell scripts\nMonitor and tune Databricks cluster performance, job scheduling, and cost optimization\nCollaborate with data scientists, analysts, and business stakeholders to understand data requirements and deliver reliable data products\nEnforce data quality, governance, and lineage best practices across the platform\nParticipate in code reviews, technical design sessions, and Agile ceremonies\nKnowledge and Experience\n6+ years of experience in data engineering or similar role\nExperience working with cloud platforms (AWS / GCP)\nFamiliarity with version control (Git) and CI/CD practices\nStrong understanding of data warehouse and data lakehouse concepts\nApache Iceberg- Experience managing Iceberg table format - 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