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. Key Responsibilities - 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 Required Skills & Experience - 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 Daily Tech Stack - Python - Spark / PySpark - Jupyter - SQL and NoSQL DBMS - Hadoop / MapReduce / Hive - Pandas