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- Tasks: Build data pipelines and support advanced analytics for real-world impact in healthcare.
- Company: Join University College London Hospitals, a leader in healthcare innovation.
- Benefits: Competitive salary, mentorship opportunities, and a chance to work on open-source projects.
- Other info: Collaborate with multidisciplinary teams and enjoy excellent career growth potential.
- Why this job: Make a difference in patient outcomes while tackling exciting data engineering challenges.
- Qualifications: Experience in data engineering with R, Python, and SQL; teamwork skills essential.
The predicted salary is between 58133 - 65261 £ per year.
Do you want to build the infrastructure that brings advanced analytics and AI from theory into reality? University College London Hospitals is looking for a Data Engineer (AI Enablement) to join the SAFEHR team. We are solving hard problems: building the secure, modern data infrastructure that lets advanced research and machine learning move from theory into reality. You will design data pipelines, evolve our data warehouse and metadata capabilities, prototype machine‑learning workflows on real clinical data, and support data quality across the Trust. Our stack includes R, Python, and SQL; with an ongoing move to a modern data platform. We develop our work as open source wherever feasible.
Responsibilities
- Design, build and maintain data pipelines that give clinicians, researchers and operational teams reliable, timely access to UCLH’s clinical data.
- Develop and improve UCLH’s data warehouse environments, including the transition from an R‑based pipeline to Spark jobs on a data platform.
- Model and transform data from our Epic electronic health record system, coordinating closely with stakeholders to maintain robust metadata.
- Specify and build reports that measure data quality.
- Develop documentation that enables the scalable, correct use of clinical datasets by reporting teams, clinical users and research projects.
- Work within a multidisciplinary team of engineers, clinicians and data scientists, and collaborate with UCL’s Advanced Research Computing Centre and Information Services teams.
- Contribute to open‑source practice through code reviews, automated testing and clear, shareable code.
- Mentor junior staff and support their development.
The position is classified at Grade 7, offering a competitive salary (£58,133 – £65,261). If you want real‑world data engineering challenges, serious technical development and a direct line from your work to patient outcomes, we’d like to hear from you. This role would also be well suited for a research software engineer with an interest in data engineering.
Advert closes on Thursday 28 May 2026.
Data Engineer (AI Enablement) | University College London Hospitals NHS Foundation Trust employer: University College London Hospital
Contact Detail:
University College London Hospital Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer (AI Enablement) | University College London Hospitals NHS Foundation Trust
✨Tip Number 1
Network like a pro! Reach out to current employees at UCLH on LinkedIn or through professional groups. Ask them about their experiences and any tips they might have for landing the Data Engineer role.
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your data pipelines, machine learning projects, or any relevant work. This will give you an edge during interviews and demonstrate your hands-on experience.
✨Tip Number 3
Practice makes perfect! Get comfortable with common interview questions related to data engineering, especially those focusing on R, Python, and SQL. Mock interviews with friends can help you nail your responses.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the UCLH team and contributing to their mission.
We think you need these skills to ace Data Engineer (AI Enablement) | University College London Hospitals NHS Foundation Trust
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Data Engineer (AI Enablement). Highlight your experience with data pipelines, R, Python, and SQL. We want to see how your skills align with our mission at UCLH!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for data engineering and how you can contribute to our team. Don’t forget to mention any open-source contributions or collaborative projects you've been part of.
Showcase Relevant Projects: If you've worked on projects that involved building data infrastructure or machine learning workflows, make sure to include them. We love seeing real-world applications of your skills, especially if they relate to healthcare!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and keep track of it. Plus, it shows you're keen on joining our team!
How to prepare for a job interview at University College London Hospital
✨Know Your Tech Stack
Make sure you’re familiar with R, Python, and SQL, as these are key to the role. Brush up on your data pipeline design and be ready to discuss how you would transition from R-based pipelines to Spark jobs.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled complex data challenges in the past. Think about specific projects where you built or improved data infrastructure, and be ready to explain your thought process and the impact of your work.
✨Understand the Clinical Context
Since this role involves working with clinical data, it’s crucial to understand the healthcare environment. Familiarise yourself with how data impacts patient outcomes and be prepared to discuss how you can contribute to that mission.
✨Emphasise Collaboration
This position requires working closely with engineers, clinicians, and data scientists. Be ready to talk about your experience in multidisciplinary teams and how you’ve successfully collaborated with others to achieve common goals.