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