About the role
About the Role
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.
We are looking for a Senior or Staff Data Engineer to join the Foundations team and build governed, trusted, reusable data products (Data 360s - e.g., Patient, Provider, Order, Clinical Result) that become the single source of truth for core business metrics across Natera. This is a hands-on, senior individual contributor role. You will write production code, make design decisions that shape how data is modeled and governed across domains, and set the engineering bar for the foundational layer that every downstream analytics, dashboard, and AI solution builds on.
The Foundations team is AI-native by design. We expect engineers to use AI coding harnesses and agentic tooling as a core part of how they design, build, test, document, and operate data products, and to help the team scale those practices. You will work in a regulated environment where data quality, lineage, and privacy are core requirements of everything we ship.
The ideal candidate is a strong software engineer first, with deep data modeling instincts, a passion for standards and governance, and the judgment to balance rigor with pragmatism. You'll work across engineering, lab operations, product, and commercial teams, so clear communication and the ability to drive alignment without direct authority matter as much as technical depth.
Key Responsibilities
Foundational Data Modeling & Architecture
- 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.