Genetics Data Engineer 1

Ownership of data engineering foundations to enable advanced human quantitative genetics analyses, ensuring data availability and quality for downstream genetic and biomedical research.

Development and maintenance of data pipelines, integration of genetic reference databases, and automation of genomic data quality control within secure, multi-platform environments including biobank Trusted Research Environments (TREs).

Optimization of analytics workflows supporting rapid-turnaround target assessments (~20-25 targets/year), integration of genetics pipelines with agentic AI workflows, and building dashboards that expose genetic evidence to stakeholders for decision-making.

Bachelor's or Master's degree in computer science, data engineering, bioinformatics, or related field.

Minimum 6 years of relevant experience in data engineering with exposure to healthcare, medical, or biological data.

Strong expertise in Python; familiarity with cloud/HPC environments (AWS, GCP, Azure), containerization (Docker, Singularity), and data pipeline orchestration tools (workflow managers).

Experience with genetic/genomic data formats (VCF, PLINK, GWAS summary statistics) and knowledge of data privacy and governance for medical/genetic data.

Experienced in building and deploying production-grade, reproducible data pipelines and tools that exceed stakeholder expectations in human genetics context.

Skilled at working across strategic and hands-on implementation levels within secure, privacy-compliant multi-platform ecosystems like biobank TREs.

Demonstrated ability to integrate genetic data engineering with AI workflows and deliver interactive data services to diverse scientific and corporate stakeholders.