Sponsorship verdict
Sponsorship possible
One solid signal, not two. Worth applying, and worth asking about sponsorship early.
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✓
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.
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The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
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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.
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✓
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.
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✓
Confirmed live todayWhen a source last listed this job as open.
Details and sources
- Employer is on a government sponsor record
- 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. Source: LCA disclosure data (US Department of Labor (OFLC)).
- No salary bar for this route
- H-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 roles
- 4 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).
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.
About the role
This is an exciting opportunity to lead a Data Engineering & Delivery (DED) team at Natera, a global leader in precision medicine and genomics testing. As a Manager of the DED team inside our Data Science Production Engineering (DSPE) department, you will have the opportunity to work with Natera's diverse and ultra-large data sets (up to PB size), develop innovative solutions with the latest information and cloud technologies, and make real impact by providing timely, accurate, and robust data products and data delivery/automation systems to support Natera's Lab Operations and Genetic Counselor/Lab Director teams, and ultimately impact patients' medical outcomes.
PRIMARY RESPONSIBILITIES:
Leadership
• Lead a Data Engineering & Delivery (DED) team, collectively develop and maintain the data infrastructure, data ingestion solutions, ETL pipelines, robust data products, and data delivery solutions, for both production support and DSPE internal applications.
• Manage the Data Engineering & Delivery (DED) team, to achieve the timely and efficient delivery of robust data products and systems that can meet DSPE business needs and project requirements.
• Measure, report, and maintain/improve operational effectiveness.
• Train and coach team members on new technologies, procedures, and guidelines/best practices.
Technical
• Lead the Data Engineering & Delivery (DED) team, collectively develop and maintain the data infrastructure, data ingestion solutions, ETL pipelines, robust data products, and data delivery solutions, for both production support and DSPE internal applications
• Translate DSPE's business needs and project requirements into technical specifications and implementation plans.
• Working with DSPE leadership teams, create design documents that can be efficiently implemented and maintained, for data system architecture, data ETL pipeline, data delivery solution, and other data systems/tools/components.
• Contribute to key development, testing, deployment, validation, maintenance, and update activities on data products and data systems/tools/components, as well as data delivery tasks.
Quality, Compliance, and Documentation
• Improve and enforce operating procedures for compliance, quality, and efficiency.
• Create training materials, and contribute to training activities, on DSPE Data Engineering & Delivery (DED) team owned data products and data systems.
Cross-Functional
• Engage proactively and directly with internal and external stakeholders to align development roadmaps, resolve dependencies, and exchange performance metrics.
• Represent the Data Engineering & Delivery (DED) team in stakeholder meetings.
QUALIFICATIONS:
• Masters degree in Statistics, Data Science, Bioinformatics, Computer Science, Mathematics, Management Science, Operational Research, or other related fields. Doctorate degrees are a plus.
• Minimal 5 years of relevant industry experience for candidates with a Master's Degree. Minimal 2 years of relevant industry experience for candidates with a Doctorate degree.
• Minimal 2 years of working experience in a highly regulated environment, e.g., CLIA and/or FDA compliant settings.
• Minimal 2 years of management experience leading a Data Engineering team, a Data Production/Delivery team, or a Data Analytics team, with demonstrable evidence of leadership and team management skills.
KNOWLEDGE, SKILLS, AND ABILITIES:
• Strong proficiency in SQL and Python programming languages. Proficiency in R is a plus.
• Proficiency in data analysis and statistical techniques.
• Proficiency in data visualization tools (e.g., Tableau, Power BI, AWS QuickSight).
• Strong SQL skills in developing robust and maintainable queries to extract and/or aggregate data from complex data sources.
• Attention to d
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