Data Engineer - ETL/PySpark (Banking Domain)
Summary
The Data Engineer will design, build, and maintain ETL pipelines and data marts within a banking environment using PySpark, Python, and Oracle SQL. The role covers the full SDLC, from development and UAT to production deployment and support.
Role Summary
We 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 Responsibilities
- Design, develop, and maintain ETL pipelines and data marts using PySpark and Python
- Write clean, maintainable, and production-grade Python code following software engineering best practices
- Own end-to-end SDLC activities: build, UAT support, UAT bug fixes, production deployment, and post-production support
- Perform data analysis and debugging using Oracle SQL and PySpark
- Work across structured, semi-structured, and unstructured data sources
- Build and maintain data warehousing solutions supporting banking/financial reporting needs
- Debug and optimize PySpark jobs for performance and reliability
- Collaborate with cross-functional teams (QA, DBAs, business analysts) through the release cycle
- Participate in CI/CD pipeline processes, including testing and validation of data pipelines
- Ensure data pipeline reliability, scalability, and adherence to banking data governance/compliance standards
Required Skills & Experience
- 5+ years of commercial experience in a data-driven engineering role
- Hands-on experience building data marts and ETL pipelines
- Expert-level PySpark and Python for ETL scripting
- Strong command of Oracle SQL for data analysis and debugging
- Proven experience across the full SDLC — build, UAT, bug fixing, deployment, post-prod support
- Strong understanding of software engineering concepts and best practices for production pipelines
- Experience working with structured, semi-structured, and unstructured data
- Prior experience with banking clients or strong banking domain knowledge
- Strong data warehousing fundamentals
Tech Stack (Daily Use)
- Languages: Python
- Big Data: Spark / PySpark, Hadoop, MapReduce, Hive
- Data Libraries: Pandas
- Databases: SQL and NoSQL DBMS
- Tools: Jupyter
- Practices: CI/CD, data testing & validation
Nice to Have (optional — add if applicable)
- Cloud experience (AWS/Azure/GCP) — not mentioned in your input, confirm with client
- Airflow or other orchestration tools
- Experience with regulatory/compliance reporting in banking