A new and exciting opportunity has arisen for a skilled Research Data Engineer to join the Bristol Research and Innovation Data Engineering (BRIDE) Hub, a pioneering collaboration between the University of Bristol, University of the West of England, and Bristol NHS Group. The BRIDE Hub sits at the forefront of health data science in the South West, bringing together expertise across academia and the NHS to transform how health data is accessed and used for research. BRIDE enables cutting-edge research, including AI and machine learning approaches to better understand disease, identify patterns and improve patient outcomes.
Working closely with the Senior Research Data Engineer, you’ll design robust, scalable data pipelines supporting the ingestion, curation, and transformation of structured and unstructured NHS data. Your work will span the full data lifecycle, from discovery and access through to harmonisation, standardisation and delivery. This is a highly collaborative role within a multidisciplinary environment of data engineers, researchers, clinicians, and informaticians where you’ll contribute directly to enabling impactful research across a diverse portfolio of projects. The role offers a rare opportunity to shape emerging data infrastructure in a rapidly evolving field, contributing to improved health outcomes at regional and national scale.
Part-time will be considered at 0.8FTE (but ideally 1.0FTE).
Hybrid working is available: 2 days in the office.
Your key responsibilities are to design, build, and maintain robust data engineering solutions that enable high quality cutting-edge health research. You’ll contribute to two core workstreams:
Workstream 1: Surfacing novel data assets
- Design and implement ETL processes to ingest and transform structured and unstructured NHS data
- Profile, map, and curate under-utilised or novel datasets (e.g. clinical text, imaging metadata, administrative data)
- Collaborate with researchers to understand requirements and make datasets discoverable and research-ready
- Improve data accessibility through efficient discovery and delivery tools
Workstream 2: Reproducible Analytic Pipelines
- Build scalable, reusable pipelines supporting end-to-end workflows from ingestion to analysis-ready outputs
- Develop and promote best practice for reproducible research, including version control, and testing and documentation
- Harmonise and standardise data across systems enabling linkage and integration
- Collaborate with data scientists to ensure pipelines support downstream analytical and AI/ML use
Across all workstreams you’ll develop modular, reusable components applicable across projects, contributing to shared codebases, technical standards, and documentation. You’ll improve tools, processes, and infrastructure, and translate complex research requirements into scalable data engineering solutions.
- You can demonstrate strong SQL proficiency, write complex queries with joins, window functions and aggregations
- You’ve solid understanding of analytical data formats and storage (e.g. Parquet, ORC, CSV, JSON) and scalable data processing
- You are motivated by building robust, re-usable data pipelines and take pride in writing clean, maintainable code
- You enjoy working in a multi-institution environment and are comfortable engaging with colleagues across academic and NHS settings
- You care about enabling high-quality, reproducible research through well-structured and reliable data assets
- You may have experience working with NHS administrative health or care data, or preparing research-ready datasets
- You may have experience working within Secure Data Environments or handling sensitive data in governed settings
Contract type: Open-ended with funding for 12 months (start date flexible, latest end date 30/11/2027)
Work pattern: Full time/ 1 FTE (hybrid, 2 days in office)
Grade: J
Salary: £43,482 -50,253 per annum
School/Unit: Bristol Medical School
Shift pattern: 35 hours per week
This advert will close at 23:59 UK time on 04/08/2026
For informal queries please contact: Rachel Denholm (Senior Lecturer), [email protected]
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