Collaborate as a senior data engineer and data architect to design build and optimize scalable data solutions using PySpark and AWS Glue in a hybrid work model. Apply extensive experience to create robust data pipelines transform complex banking data and ensure high quality data for analytics and regulatory reporting while supporting retail banking initiatives and day shift operations.
Responsibilities
• Design and implement highly scalable data pipelines using PySpark and AWS Glue to process large volumes of structured and semi structured data with a robust focus on reliability and performance optimization
• Architect end to end data ingestion frameworks that integrate various source systems into centralized data platforms ensuring consistent data modeling and governance aligned with enterprise standards
• Optimize existing batch and near real time data workflows by analyzing bottlenecks and applying tuning techniques that improve execution time resource utilization and overall system stability
• Collaborate with business and analytics stakeholders to translate complex retail banking data requirements into clear technical specifications that guide robust data engineering and architecture solutions
• Develop reusable data transformation components and metadata driven frameworks that reduce duplication improve maintainability and support rapid onboarding of new data sources
• Implement comprehensive data quality checks and validation rules within pipelines to ensure accuracy completeness and timeliness of data used for reporting risk analysis and customer insights
• Document data models pipeline designs and operational procedures in a structured and accessible manner so cross functional teams can understand lineage dependencies and usage across the data ecosystem
• Coordinate with cloud infrast
📌 AWS Glue + Pyspark (Chennai)
🏢 Cognizant
📍 Chennai
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