# Data Engineer - ETL/PySpark (Banking Domain)

> GSSTech Group · Dubai, United Arab Emirates · — · Posted 2026-08-21

**Workplace:** on_site

## Description

### 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

## Apply

[Apply at GSSTech Group](https://apply.workable.com/gsstech-group/j/B32B3CF4C7/apply)

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