Data Engineer - ETL/PySpark (Banking Domain)
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Key skills for this role
Role Overview
Build and maintain data marts and ETL pipelines within a banking environment.
Own the full SDLC from build through UAT, deployment, bug fixing, and postproduction support.
Key Skills for This Role
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Role Summary
Build and maintain data marts and ETL pipelines within a banking environment.
Own the full SDLC from build through UAT, deployment, bug fixing, and postproduction support.
Key Responsibilities
- Design, build, and maintain ETL pipelines and data marts using PySpark and Python.
- Write clean, maintainable, robust production-grade code.
- Own 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 and validation activities.
Required Skills and Experience
- At least five years of commercial experience in a data-driven role.
- Hands-on experience building data marts and ETL pipelines.
- Expert-level Python expertise for ETL scripting.
- Strong PySpark experience.
- Analytical expertise in Oracle SQL and data analysis.
- Understanding of software engineering concepts and production pipeline best practices.
- Banking client or banking domain knowledge.
- Strong data warehousing fundamentals.
- Familiarity with query languages and SQL and NoSQL database technologies.
- CI/CD exposure, including testing and validation of data pipelines.
Daily Technology Stack
- Python, Spark, and PySpark.
- Jupyter.
- SQL and NoSQL database management systems.
- Hadoop, MapReduce, and Hive.
- Pandas.
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