Data Engineer - ETL PySpark Banking Domain

Role Summary

We are looking for a hands-on Data Engineer with strong PySpark and Python expertise to build and maintain data marts and ETL pipelines within a banking environment. The ideal candidate owns the full SDLC, from build through UAT, bug fixing, production deployment, and postproduction support.

• Design, build, and maintain ETL pipelines and data marts using PySpark and Python
• Write clean, maintainable, and robust production-grade code
• Own end to end SDLC activities: build, UAT, UAT bug fixes, production deployment, and postproduction support
• Perform Oracle query analysis and PySpark code debugging
• Work across structured, semi structured, and unstructured data sources
• Apply software engineering best practices to production pipelines
• Support CI/CD processes and data testing/validation activities

• 5+ years commercial experience in a data-driven role
• Hands-on experience building data marts and ETL pipelines
• Expert level Python for ETL scripting
• Strong PySpark experience
• Analytical expertise in Oracle SQL and data analysis
• Understanding of software engineering concepts and best practices for production pipelines
• Banking client or banking domain knowledge
• Strong Data Warehousing fundamentals
• Familiarity with query languages and both SQL and NoSQL database technologies
• CI/CD exposure, including testing and validation of data pipelines

• Python
• Spark / PySpark
• Jupyter
• SQL and NoSQL DBMS
• Hadoop / MapReduce / Hive
• Pandas