This hybrid role combines data engineering and data architecture responsibilities for cloud based analytics solutions using PySpark and Databricks platforms. The role focuses on designing scalable data pipelines optimizing Databricks SQL workloads and ensuring robust data models that support business analytics across life and annuities insurance domains. The candidate will collaborate with cross functional teams during day shifts to deliver high quality data products that enable informed deci
Responsibilities
• Design and implement scalable data pipelines using PySpark to ingest transform and validate large volumes of structured and semi structured data within Databricks workspaces ensuring reliable and reusable data assets across business units.
• Develop and maintain optimized Databricks SQL queries and data models that support analytics reporting and self service data consumption focusing on performance tuning and cost effective resource utilization in the shared compute environment.
• Architect end to end data solutions by defining data lake and warehouse structures partitioning strategies and storage conventions so that data remains consistent discoverable and compliant with enterprise data governance practices.
• Collaborate closely with data analysts business stakeholders and application teams to translate analytical and regulatory requirements into robust technical designs that improve accuracy timeliness and usability of critical data sets.
• Implement rigorous data quality frameworks including profiling validation rules and automated checks in PySpark and Databricks workflows to reduce data defects and ensure trustworthy information for downstream analytics consumers.
• Configure and maintain Databricks jobs clusters and notebooks by establishing standards for environment configuration code packaging and monitoring so that pipelines run predictably and are straightforward to troubleshoot.
• Optimize data processing workloads by analyzing job
📌 Data Engineer (Kolkata)
🏢 Cognizant
📍 Kolkata
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