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
Design and build the Data 360 data products: governed, well-documented, dimensional and domain-driven models that integrate clinical, laboratory, operational, and commercial data into consistent, trusted entities.
Define and maintain canonical definitions for core business metrics (e.g.,: volume, turnaround time, first-pass yield) so they are calculated once, certified, and reused everywhere.
Own design and architecture decisions for the foundational layer; lead design: reviews and make build-vs-buy and technology decisions in partnership with the data platform and architecture teams.
Model data across a wide range of sources and types, including structured: relational data, semi-structured (JSON/event) data, lab and LIMS data, genomic and result data, and third-party and CRM data, and reconcile conflicting definitions and identifiers across systems.
Build and maintain semantic layers and certified, trusted views of core: metrics that enable self-service analytics with consistent definitions.
Use Sigma to publish foundational datasets, governed metrics, and reusable: workbook components that business teams can build on with confidence.
What they're looking for
- Design data models with downstream consumption in mind: performance, discoverability, and ease of use for analysts, dashboards, and LLM-based querying.
- Build and maintain scalable, tested, well-documented transformation pipelines: in dbt on Snowflake, applying software engineering best practices: modularity, version control, code review, automated testing, and CI/CD.
- Write production-grade SQL and Python; contribute to shared frameworks,: templates, and macros that make it faster and easier for the whole team to deliver new data products consistently.
- Lead root-cause analysis on complex data issues, such as metric: discrepancies, event-definition mismatches, and upstream data gaps, and drive durable fixes rather than one-off patches.
