Jobs / United States / Natera INC
Director, Data Product Engineering
Natera INC · 🇺🇸 US Remote · Remote
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 48 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 12 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Natera INC paid sponsored hires in similar roles4 certified filings for “Senior Data Science Production Engineer” (Data Scientists) in CA: $115k–$195k, median $144k. Most were filed at wage level III (50%) — 3 lottery entries, ≈46% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
Or apply yourself on the official page →
Sponsor Radar — Natera INC
The US Department of Labor certified 48 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 12 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Natera INC →
About the role
ABOUT THE ROLE 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. Critical Priorities for This Role • 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. RESPONSIBILITIES 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. • Implement agentic data quality: automated drift detection, root-cause identification, huma