The opportunity
Natera is a global leader in cell-free DNA testing, serving patients across oncology, women’s health, and organ health. Our Data & AI organization builds the enterprise data platform and AI systems that turn clinical, genomic, and operational data into products that improve patient care and accelerate the business.
What you'll do
Own the technical design and architecture for the analytics and AI solutions: your team delivers; drive design reviews and make build-vs-buy and technology decisions in partnership with the data platform and architecture teams.
Translate business stakeholder needs into well-scoped technical designs,: breaking down complex analytics initiatives into deliverable milestones for the team.
Establish and enforce engineering standards for modeling, pipeline design,: testing, CI/CD, and observability so solutions are built consistently and reusably across business domains.
Design, build, and maintain scalable data pipelines and transformations on: our cloud data platform, sourcing clinical, laboratory, operational, and commercial data to serve business analytics.
Develop governed data models and semantic layers that power self-service: analytics, dashboards, and data products (e.g., Patient 360, Provider 360, Test 360) for business teams.
Develop AI solutions to enable business productivity and automation through: use of agentic workflows, RAG/retrieval, and LLM pipelines (extraction/classification)
What they're looking for
- Write production-grade SQL and Python; contribute to shared frameworks,: templates, and tooling that let the team deliver new analytics solutions faster.
- Ensure the data layer supports performant, trustworthy analytics and LLM: querying ; lead root-cause analysis on complex data issues and drive durable fixes.
- Use AI coding assistants and agentic tools daily across the development: lifecycle—design, coding, testing, documentation, and operations—and measurably increase your own and the team’s throughput.
- Define and scale the team’s AI-native engineering practices: prompt and context patterns, reusable agent workflows, AI-assisted testing and review, and the guardrails that keep quality and compliance intact.