Requirements
Requires 5+ years of commercial experience in data engineering roles, with expert-level PySpark and Python for ETL scripting. Must have a strong command of Oracle SQL for data analysis and debugging, proven experience across the full SDLC, and a strong understanding of software engineering concepts for production pipelines. Experience with structured, semi-structured, and unstructured data, along with banking client experience or strong banking domain knowledge, and solid data warehousing fundamentals are essential.
Description
Job description / Role
Job Type
Full Time
Job Location
Dubai, UAE
Nationality
Any Nationality
Salary
Not Specified
Gender
Not Specified
Arabic Fluency
Not Specified
Job Function
IT - Software & Web Development
Company Industry
Finance, Investment & Asset Management
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
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