Job Opportunities API

Location: Oxford or London (Hybrid) The Mission: Why We Exist Genomics is a science-led transatlantic TechBio combining large-scale genetic and health data with proprietary analytics to accelerate drug discovery and advance predictive, preventative healthcare. We are united by a single vision to help people live longer healthier lives, using the power of genomics. Genomics aims to help people live longer, healthier lives in two ways: super-charging drug discovery and development for novel treatments with our AI-enabled advanced genetic analytics platform, and by helping people understand their personal risk of common chronic diseases through polygenic risk scores - giving doctors and health systems the chance to get the right people into the right prevention, screening and treatment programmes at the right time. Role Purpose Data is the foundation of everything we do. Our Data Engineering team owns the end-to-end journey of the genomic datasets that power both our commercial products and our internal science – from disease-risk insights that reach real patients, to the discovery of novel therapeutic targets. As a Data Engineer in this small, high-impact team, you'll turn large, complex and scientifically consequential datasets into trusted, analysis-ready resources. Your focus will be data transformation, harmonisation and quality control: working within established automated workflows, but bringing the statistical-genetics judgement to know when the numbers don't add up – and the curiosity to find out why. As you grow, you'll take on broader work – helping define schemas for new data types and shaping how we expand our genomic data resource. It's a genuine intersection of data engineering, data modelling and genetics, with real scope to develop across both technical and scientific dimensions. A Day in the Life - Moving data in: ingesting large-scale genetic and genomic datasets – GWAS summary statistics, individual-level genotype and phenotype data – through a mix of scripting, automated pipelines and hands-on processing in a Linux environment. - Safeguarding quality: interpreting QC metrics and diagnostic plots, investigating anomalies, and applying sound scientific judgement to resolve data-quality issues. - Getting the detail right: configuring workflow parameters and curating dataset metadata so every dataset is correctly represented, fit for scientific use, and clearly described for internal scientists and external customers. - Collaborating: partnering with software engineers and developers in an agile setting to diagnose pipeline issues, define requirements and continuously improve our ingestion and QC processes. - Growing: contributing to schema design for new data types and helping expand our genomic data resource alongside science, product and engineering. Who You Are - Grounded in human statistical genetics, with hands-on experience of GWAS summary statistics and/or large-scale individual-level genotype and phenotype data. - Proficient in Python and Unix/Linux, and able to point to real bioinformatics or data-engineering work in a research or commercial setting. - Confident reading complex data-quality outputs, and able to use scientific judgement to investigate and resolve issues. - Well organised – happy to plan, prioritise and deliver across competing tasks at pace. - A strong communicator who works well across multi-disciplinary teams and just as effectively on your own. - Educated to BSc or higher in a relevant discipline – bioinformatics, computational biology, human genetics or similar – or with equivalent experience Your Package We are committed to providing a transparent, supportive, and rewarding work environment. COMPENSATION & GROWTH - Competitive Salary: Salaries are externally benchmarked annually to ensure top-of-market compensation. - Clear Career Path: A straightforward, open progression framework means you'll always know the path to promotion