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. 

The Data Science team at Qureight turns imaging and clinical data into evidence that accelerates clinical trials. We combine Qureight’s quantitative CT biomarkers with clinical data from sponsors, CROs and research cohorts, using statistical modelling, longitudinal analysis and machine learning. Our work helps biopharma sponsors choose endpoints that detect change earlier, build external control arms from real-world data, and design smaller and smarter trials.  

About the role 

Our Data Scientists work across a growing number of sponsor projects and research studies, each with their own datasets, cohorts and analysis plans. We receive clinical data from sponsors and CROs in a different format for each study, often only at database lock. Much of the Data Scientists’ time is spent preparing, reshaping and re-running data by hand, which slows delivery and makes results harder to reproduce. We want to change that. 

We’re hiring a Senior Clinical Data Engineer to transform incoming clinical trial data into standardised, analysis-ready datasets. You will build reproducible data pipelines that take repetitive data work away from our Data Scientists, increasing their output and making every result traceable to its inputs, code and parameters. You will work alongside the Data Scientists, developing an in-depth understanding of their analyses and turning recurring patterns into shared, tested and versioned tooling. The Senior Clinical Data Engineer will also work closely with Qureight’s Data Engineering team, building on their existing study pipelines and sharing best practice and ideas so that both teams build on common standards. 

If you enjoy helping facilitate effective and efficient working practices, and you’re interested in the interface between clinical data standards, data engineering, data science and medical applications (particularly in clinical trials), then this role is for you. 

 What you will do 

• 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 

Requirements

What we need 

• 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 

Even better if you have… 

• 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 
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