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.
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