The opportunity
Natera is seeking a product engineering leader to build and lead the team that designs, delivers, and operates domain data products and AI-enabled analytical solutions on NDP (Natera Data Platform). You will report to the Head of Data & AI and partner with platform, governance,…
What you'll do
Lead the build of cross functional data products, analytics experiences, and: Golden KPI's that are essential to making data driven decisions across the business
Create the operating model to deliver analytics to business users by: leveraging embedded data/AI engineers with each business domain.
Track and report delivery KPIs: time-to-delivery, certified dataset count, adoption by consuming teams, open production incidents.
Define and enforce the publishing standard for analytics products: semantically correct, AI-ready, lineage documented, ownership assigned, catalog entry complete.
Establish the headless data product standard: domain-owned assets any authorized consumer — dashboard, workflow, or AI system — can use.
Build golden paths (templates, examples, documented patterns) so every: product starts from a known-good baseline, and make this the operating model for intake, build, certification, and support.
What they're looking for
- + years in data engineering, 5+ leading data or analytics engineering teams at Director level.
- Hands-on depth: you write and review Python and SQL, critique dbt models, debug pipeline failures, and make architecture calls yourself.
- Shipped data products to production with measurable adoption. You can name: the products, who used them, and what changed.
- Regulated-environment delivery (healthcare, life sciences, diagnostics,: pharma) with PHI and real HIPAA compliance experience.
- Modern data stack: Snowflake, AWS, Claude, dbt, Fivetran, Sigma, orchestrator such as Airflow or Dagster; CI/CD for data pipelines and infrastructure as code.
- Working knowledge of data mesh and headless, domain-owned data products built for human and AI consumers.
- Built or led teams using AI-assisted development in production: agents, code generation, AI-driven testing and validation.
- Defined engineering standards, golden paths, or operating models that scaled across multiple teams or domains.
