Senior Data Engineer, Data Foundations

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.

Semantic Layer & Trusted Views

• 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.
• Design data models with downstream consumption in mind: performance, discoverability, and ease of use for analysts, dashboards, and LLM-based querying.

Hands-On Data Engineering

• 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.

Data Standards, Quality & Governance

• Establish and enforce standards for naming, modeling, testing, documentation, and certification of data products.
• Implement data quality checks, data contracts, lineage, and monitoring so downstream consumers can trust the data by default.
• Ensure data handling of PHI and genomic data meets HIPAA, CLIA, and internal security and governance requirements, including access controls, masking and semantic tagging, auditability, and retention.
• Partner with governance, security, and data platform teams on metadata, cataloging, and discoverability.
• Treat validation as part of the definition of done. Validate every data product before release by reconciling to source systems, checking grain, uniqueness, and referential integrity, comparing metrics against existing or known-good reports, and confirming results with domain experts. Document the results so consumers know what has been verified.

AI-Native Engineering

• Use AI coding assistants and agentic tools daily across design, coding, testing, documentation, and operations, and measurably increase your own and the team's throughput.
• Build the data foundations that make AI work: well-modeled, well-described, governed data and semantic context that LLMs and agents can query accurately and safely.

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