Description
Role SummaryWe are looking for a hands-on Data Engineer with strong ETL and PySpark expertise to design, build, and support data pipelines and data marts within a banking environment. The ideal candidate will own the full SDLC lifecycle — from build through UAT, production deployment, and post-production support — while working across structured, semi-structured, and unstructured data.Key ResponsibilitiesDesign, develop, and maintain ETL pipelines and data marts using PySpark and PythonWrite clean, maintainable, and production-grade Python code following software engineering best practicesOwn end-to-end SDLC activities: build, UAT support, UAT bug fixes, production deployment, and post-production supportPerform data analysis and debugging using Oracle SQL and PySparkWork across structured, semi-structured, and unstructured data sourcesBuild and maintain data warehousing solutions supporting banking/financial reporting needsDebug and optimize PySpark jobs for performance and reliabilityCollaborate with cross-functional teams (QA, DBAs, business analysts) through the release cycleParticipate in CI/CD pipeline processes, including testing and validation of data pipelinesEnsure data pipeline reliability, scalability, and adherence to banking data governance/compliance standardsRequired Skills & Experience5+ years of commercial experience in a data-driven engineering roleHands-on experience building data marts and ETL pipelinesExpert-level PySpark and Python for ETL scriptingStrong command of Oracle SQL for data analysis and debuggingProven experience across the full SDLC — build, UAT, bug fixing, deployment, post-prod supportStrong understanding of software engineering concepts and best practices for production pipelinesExperience working with structured, semi-structured, and unstructured dataPrior experience with banking clients or strong banking domain knowledgeStrong data warehousing fundamentalsTech Stack (Daily Use)Languages: PythonBig Data: Spark / PySpark, Hadoop, MapReduce, HiveData Libraries: PandasDatabases: SQL and NoSQL DBMSTools: JupyterPractices: CI/CD, data testing & validationNice to Have (optional — add if applicable)Cloud experience (AWS/Azure/GCP) — not mentioned in your input, confirm with clientAirflow or other orchestration toolsExperience with regulatory/compliance reporting in banking