Data Engineer - Python, SQL, Airflow, Dbt

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.