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# Data Engineer

- **Company:** University of Oxford
- **Location:** Oxford, UK (Remote available)
- **Salary:** £39,424.0 - £51,983.0
- **Contract:** Temporary contract
- **Skills:** Bioinformatics, Extract Transform Load (ETL), Interoperability, Python (Programming Language), Metadata Standards, Data Strategy, Information Technology, Data Management, Data Pipelines
- **Published:** August 14, 2026
- **Apply:** https://www.jobs.service.gov.uk/jobs/6a7ddabe0e0eda050e234871/apply

## About the Role

It is essential that you hold Master's degree in Computer Science, Bioinformatics, Data Science or another relevant computational discipline together with experience in designing and maintaining ETL workflows and handling large, complex or heterogeneous datasets. You will have strong proficiency in Python, with experience in building production-grade data pipelines. You will be able to manage multiple concurrent projects and deliver high-quality results to deadlines. Having a working knowledge of biology with a clear interest in biomedical data management is highly desirable.

## Description

Are you a skilled Data Engineer looking to apply your expertise to real-world scientific challenges? Join the Research Informatics Team at the Centre for Medicines Discovery and help power cutting-edge drug discovery and translational science.

As a Data Engineer, you will play a central role in designing and delivering scalable data pipelines and systems that enable scientists to work efficiently with complex datasets. You will collaborate closely with multidisciplinary teams across the Centre for Medicines Discovery, the Centre for Human Genetics, and the wider Nuffield Department of Medicine. You will also contribute to high-profile initiatives such as OpenBind UK, working alongside leading organisations including Diamond Light Source and EMBL-EBI. You will design, implement, and maintain scalable ETL pipelines, incorporating automation, validation, quality checks, and workflow orchestration. You will also develop performant and reliable data products and services, contribute to FAIR-compliant data management strategies, and support improvements in metadata standards, interoperability, and data provenance.


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