Data engineer in clinical imaging

Qureight's mission is to accelerate clinical trials and ensure breakthroughs in lung and heart disease reach patients without delay. Our AI-powered data and imaging curation platform enables the analysis of clinical imaging and other healthcare data, helping our customers bring treatments to market, faster. We're looking for talented people who want their work to matter. With offices in Cambridge and London, you'll join our multidisciplinary team of clinicians, scientists, and engineers. What unites us is our open culture, continuous learning mindset, and a shared mission to help biopharma run faster, smarter trials.

• Prepare clinical trial data from sponsors and CROs to each study's data transfer specification
• Build analysis-ready datasets for modelling and machine learning (ADaM-style where required) from derivation specifications agreed with the Data Scientists
• Design, build and maintain reproducible data pipelines that combine imaging biomarkers and clinical data, building on the Data Engineering team's existing Dagster study pipelines, analytics workflow libraries and Qureight platform
• Build and test pipelines against dummy or interim data before database lock, and deliver CDISC formats (SDTM, ADaM) where required
• Work alongside Data Scientists to understand their analytical workflows and automate repetitive steps so that they can spend more of their time on the science
• Build reproducibility and quality in ‘by design': versioned data, code and environments, and introducing clear lineage and validation checks in every pipeline so issues are caught early and analyses run on trusted data
• Develop shared tooling, templates and libraries that standardise how analyses are set up, run and reviewed, and help Data Scientists move code from notebooks into tested workflows
• Make sure pipelines support the traceability and change control expected in a regulated, GxP environment

• Proven experience as a Data Engineer or clinical data programmer, ideally working closely with data scientists, analysts or researchers
• Hands-on experience with CDISC standards, in particular SDTM and ADaM, on clinical trial data
• Experience handling clinical trial data from sponsors or CROs: data transfers, EDC extracts, visit structures and database lock
• Strong Python skills gained in a commercial setting, including the scientific Python stack e.g. pandas, NumPy, pandera and pytest
• Experience building and maintaining production data pipelines with automated testing and CI/CD
• Experience implementing data validation and quality checks
• Experience with workflow orchestration tools, such as Dagster, and cloud infrastructure on AWS
• A good working understanding of data science workflows, including statistical modelling and machine learning and of how clinical trial data is structured and used: endpoints, visit schedules and missing data.
• Strong collaboration and communication skills, with a service-minded collaborative approach and the curiosity to understand where data comes from and how it will be used
• Будет плюсом: Experience with data versioning and experiment tracking tools e.g. DVC, MLflow, or Dagster asset lineage; Experience preparing data for machine learning and longitudinal modelling, for example feature tables and train/test splits that avoid leakage; Experience with containerisation and reproducible environments e.g. Docker, Kubernetes; Experience with medical imaging data e.g. CT or MRI in DICOM format or clinical trial data; Experience working in regulated, GxP or ISO 13485 environments; Experience in respiratory or interstitial lung disease trials

• A comprehensive benefits package that includes an annual bonus plan, private medical insurance, life insurance, cycle-to-work scheme, and a contributory pension scheme
• Equity, so that our team can share in the long-term success of Qureight
• 28 days' holiday allowance, 25 days' annual leave and 3 additional company days between Christmas and New Year, plus bank holidays and enhanced family leave
• A diverse work environment that brings together experts in many fields, including software engineering, DevOps, data science, machine learning, quality assurance, regulatory affairs, and clinical operations.