Data Engineer - Python, SQL, Airflow, dbt - Banking

Data Engineer - Python, SQL, Airflow, dbt - Banking

An established bank is looking for a hands-on Data Engineer to help design, build and maintain a scalable on-premise data warehouse and modern data engineering platform.

This is a strong opportunity for someone who enjoys building robust data pipelines, working close to the infrastructure, and supporting business-critical analytics and reporting. The environment is non-cloud / on-prem, so this will suit someone comfortable working with Unix/Linux, scheduling, scripting, deployment and production support.

You will work with Python, SQL, Apache Airflow and dbt, while also supporting a wider Microsoft BI environment including SSIS, SSRS, SSAS and T-SQL.

You will be responsible for designing and building reliable data pipelines, developing transformation logic, maintaining data models, and supporting the bank’s analytics and reporting platforms.

Key responsibilities include:

• Designing and building ETL/ELT pipelines using Python and SQL
• Developing and orchestrating workflows using Apache Airflow
• Building and maintaining dbt models, macros, tests and documentation
• Working in a Unix/Linux environment for scheduling, scripting and deployment
• Supporting CI/CD pipelines and version control processes
• Translating business requirements into clear technical specifications
• Administering and supporting data analytics platforms
• Building and maintaining solutions across SSIS, SSRS, SSAS and T-SQL
• Supporting dashboards, reporting and visualisation requirements
• Performing testing, troubleshooting and issue resolution
• Producing clear technical documentation
• Working closely with stakeholders across technology, data, analytics and business teams
• Operating in line with the bank’s risk, compliance and change-control frameworks

The ideal candidate

You do not need to tick every box, but you should have strong hands-on data engineering experience and be comfortable working in a controlled, production-focused environment.

We are particularly interested in people with experience across:

• Strong Python programming for data pipelines, APIs and scripting
• Advanced SQL, ideally T-SQL or PL/SQL
• Apache Airflow, including DAG configuration, maintenance and optimisation
• dbt, including models, macros, tests and documentation
• ETL/ELT design and data warehousing
• Unix/Linux environments
• On-premise or infrastructure-aware data platforms
• CI/CD, version control and test automation
• Docker or containerisation
• Microsoft BI stack: SSIS, SSRS, SSAS and T-SQL
• Power BI, Tableau, Qlik or similar reporting/visualisation tools

Banking or financial services experience would be useful, particularly if you have worked in a regulated environment with strong governance, auditability, data quality and change-control requirements. However, strong hands-on data engineering experience is the priority.

Good fit for someone who is

• A practical, hands-on Data Engineer
• Comfortable owning production data pipelines
• Strong technically, but able to work with business stakeholders
• Used to controlled environments where documentation, testing and governance matter
• Comfortable with both modern data engineering tooling and established BI platforms
• Interested in building reliable, scalable data solutions rather than just dashboards

Data Engineer, BI Data Engineer, Data Warehouse Engineer, Python, SQL, T-SQL, PL/SQL, Apache Airflow, Airflow DAGs, dbt, ETL, ELT, data warehouse, data pipelines, Unix, Linux, Docker, CI/CD, SSIS, SSRS, SSAS, Microsoft BI, Power BI, Tableau, Qlik, banking, financial services, regulated environment.