At a Glance
- Tasks: Design and build scalable data pipelines using Python, SQL, and Apache Airflow.
- Company: Established bank with a focus on data engineering and analytics.
- Benefits: Competitive salary, hands-on experience, and opportunities for professional growth.
- Other info: Join a dynamic team with a focus on innovation and compliance.
- Why this job: Make a real impact in banking by building robust data solutions.
- Qualifications: Experience in data engineering, Python, SQL, and working in Unix/Linux environments.
The predicted salary is between 60000 - 80000 € per year.
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 - Python, SQL, Airflow, dbt - Banking in London employer: Rothstein Recruitment
Join an established bank that values innovation and collaboration, offering a dynamic work environment where you can thrive as a Data Engineer. With a strong focus on employee growth, the bank provides opportunities for professional development and encourages a culture of continuous learning. Enjoy the unique advantage of working in a controlled, production-focused setting that prioritises data quality and governance, while being part of a team that supports critical business analytics and reporting.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer - Python, SQL, Airflow, dbt - Banking in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the banking and data engineering space. Attend meetups, webinars, or even just grab a coffee with someone in the industry. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data pipelines, ETL processes, or any projects you've worked on using Python, SQL, or Airflow. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge, especially around dbt, CI/CD, and the Microsoft BI stack. Be ready to discuss how you've tackled challenges in previous roles and how you can contribute to their data engineering needs.
✨Tip Number 4
Don't forget to apply through our website! We’ve got some fantastic opportunities waiting for you. Tailor your application to highlight your hands-on experience in data engineering and your comfort with production environments. Let's get you that dream job!
We think you need these skills to ace Data Engineer - Python, SQL, Airflow, dbt - Banking in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your hands-on experience with Python, SQL, and Apache Airflow, as well as any relevant banking or financial services background.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're the perfect fit for this Data Engineer role. Share specific examples of how you've designed and built data pipelines or worked in Unix/Linux environments to show off your expertise.
Showcase Your Technical Skills:Don’t just list your technical skills; demonstrate them! Include projects or achievements that showcase your experience with dbt, ETL/ELT design, and CI/CD processes. We love seeing real-world applications of your skills.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it makes the process smoother for everyone involved!
How to prepare for a job interview at Rothstein Recruitment
✨Know Your Tech Stack
Make sure you’re well-versed in Python, SQL, and Apache Airflow. Brush up on your ETL/ELT processes and be ready to discuss how you've built data pipelines in the past. The more specific examples you can provide, the better!
✨Understand the Banking Environment
Familiarise yourself with the banking sector's regulations and compliance requirements. Be prepared to talk about how you’ve navigated these in previous roles, especially if you’ve worked in a controlled environment before.
✨Showcase Your Problem-Solving Skills
Be ready to discuss challenges you've faced in data engineering and how you resolved them. Whether it’s troubleshooting a data pipeline or optimising a workflow in Airflow, concrete examples will demonstrate your hands-on experience.
✨Communicate with Stakeholders
Highlight your ability to translate technical jargon into business language. Discuss how you’ve collaborated with non-technical teams to ensure everyone is on the same page regarding data needs and project goals.
