Senior Clinical Data Engineer

Qureight Ltd; Greater London; On-site; GBP 85,000 - 110,000; Full time

Job summary

Qureight Ltd in the UK is hiring a Senior Clinical Data Engineer to transform incoming clinical trial data into standardised, analysis-ready datasets. You will build reproducible data pipelines that empower Data Scientists and ensure traceability to inputs, code, and parameters.

You will collaborate with Data Scientists and Data Engineering, work with CDISC standards (SDTM/ADaM), and contribute to a regulated, GxP environment while expanding the platform for imaging and clinical data analyses.

Qualifications

• Proven experience as Data Engineer in clinical data environments.
• Hands-on CDISC SDTM/ADaM experience with sponsor or CRO data.
• Experience handling clinical trial data transfers, EDC extracts, and database lock.
• Strong Python skills in a commercial setting (pandas, NumPy, pandera, pytest).
• Experience building production data pipelines with automated testing/CI/CD.

Responsibilities

• Prepare trial data from sponsors/CROs to study data transfer specs.
• Build analysis-ready datasets (ADaM-style where required) from specs.
• Design and maintain reproducible data pipelines combining imaging biomarkers and clinical data.
• Deliver CDISC formats (SDTM/ADaM) where required.
• Collaborate with Data Scientists to automate repetitive steps and enhance science time.
• Ensure traceability, versioning, and validation checks across pipelines.
• Develop shared tooling and templates to standardise analyses and pipelines.

Skills

Python

Data Engineering

Collaboration

Communication skills

Machine learning

Education

CDISC SDTM/ADaM experience

Tools

Dagster

CI/CD

Pandas

NumPy

pandera

pytest

Job description

About us

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

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... Experi