Director, Data Product Engineering US Remote

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, and product functions to turn data needs into certified, production-grade data and analytics products.

Natera follows a data mesh architecture with headless data products: domain-owned assets, not tied to any BI layer, built for both human and AI consumption. A data product is ready when it is semantically correct and AI-ready, not just numerically accurate. Your mandate is to make that standard repeatable across every domain while transforming how the team works: AI-native, 3–5x more productive, and self-service for the business.

Please note that this is role focuses on product side of data engineering (not platform). This person needs to demonstrate their ability to

(a) Build a self-service analytics product experience for business users and

(b) Create a catalog of AI ready gold-standard cross functional data products

(c) Create an operating model focusing on reusability, speed. and business value of data

that scale beyond one business domain.

• Standardize and scale how data products are built — one publishing standard, golden paths, and a common operating model across every domain.
• Make the team AI-native — agentic SDLC and AI-assisted development become how everyone works resulting in higher productivity and growth opportunities
• Deliver a 3–5x productivity gain — instrumented with baselines and delivery KPIs, not anecdotes.
• Raise data product quality — semantically correct, AI-ready, observable, ship-ready data products.
• Make analytics self-service — certified, discoverable products that business users and AI systems consume without an engineering queue.

1. Own Data Product Delivery

• 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.

2. Standardize & Scale How Data Products Are Built for Analytics and AI use

• 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.

3. Build an AI-Native Engineering Competency

• Drive agentic data engineering as the default: agentic SDLC, AI pipeline generation, agents writing and validating dbt models, AI-driven testing, LLM tools in code review and documentation.
• Train every engineer to work AI-natively and redefine the working model — SDLC steps, roles, human-in-the-loop checkpoints, definition of done.
• Own a measurable plan to lift productivity 3–5x: baseline throughput and cycle time, instrument each change, report outcomes.

4. Raise the Data Product Quality Bar

• Enforce engineering standards on every production solution: CI/CD for pipelines, infrastructure as code, data observability, data quality frameworks.
• Meet HIPAA, RAQA, and data classification requirements at design time. If a product does not meet the bar, it does not ship.

5. Make Data…