Director, Data Product Engineering at Natera

<h1><span style="color: #0b2545;"><strong>ABOUT THE ROLE</strong></span></h1> <p>&nbsp;</p> <p><span style="color: #1a1a1a;">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 &amp; AI and partner with platform, governance, and product functions to turn data needs into certified, production-grade data and analytics products.</span></p> <p>&nbsp;</p> <p><span style="color: #1a1a1a;">Natera follows a data mesh architecture with headless data products: domain-owned assets, <u>not tied to any BI layer, built for both human and AI consumption</u>. 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.</span></p> <p>&nbsp;</p> <p><span style="color: #1a1a1a;">Please note that this is role focuses on product side of data engineering (not platform). This person needs to demonstrate their ability to </span></p> <p>&nbsp;</p> <p><span style="color: #1a1a1a;">(a) Build a self-service analytics product experience for business users and</span></p> <p><span style="color: #1a1a1a;">(b) Create a catalog of AI ready gold-standard cross functional data products</span></p> <p><span style="color: #1a1a1a;">(c) Create an operating model focusing on reusability, speed. and business value of data </span></p> <p>&nbsp;</p> <p><span style="color: #1a1a1a;">that scale beyond one business domain.</span></p> <p>&nbsp;</p> <p><span style="color: #0b2545;"><strong>Critical Priorities for This Role</strong></span></p> <ul> <li style="color: #000000 !important;"><span style="color: #1a1a1a;"><strong>Standardize and scale how data products are built — </strong>one publishing standard, golden paths, and a common operating model across every domain.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;"><strong>Make the team AI-native — </strong>agentic SDLC and AI-assisted development become how everyone works resulting in higher productivity and growth opportunities</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;"><strong>Deliver a 3–5x productivity gain — </strong>instrumented with baselines and delivery KPIs, not anecdotes.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;"><strong>Raise data product quality — </strong>semantically correct, AI-ready, observable, ship-ready data products.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;"><strong>Make analytics self-service — </strong>certified, discoverable products that business users and AI systems consume without an engineering queue.</span></li> </ul> <p>&nbsp;</p> <h1><span style="color: #0b2545;"><strong>RESPONSIBILITIES</strong></span></h1> <p><span style="color: #0e7c7b;"><strong>1. Own Data Product Delivery</strong></span></p> <ul> <li style="color: #000000 !important;"><span style="color: #000000;">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</span></li> <li><span style="color: #1a1a1a;">Create the operating model to deliver analytics to business users by leveraging embedded data/AI engineers with each business domain.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Track and report delivery KPIs: time-to-delivery, certified dataset count, adoption by consuming teams, open production incidents.</span></li> </ul> <p><span style="color: #0e7c7b;"><strong>2. Standardize &amp; Scale How Data Products Are Built for Analytics and AI use</strong></span></p> <ul> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Define and enforce the publishing standard for analytics products: semantically correct, AI-ready, lineage documented, ownership assigned, catalog entry complete.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Establish the headless data product standard: domain-owned assets any authorized consumer — dashboard, workflow, or AI system — can use.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">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.</span></li> </ul> <p><span style="color: #0e7c7b;"><strong>3. Build an AI-Native Engineering Competency</strong></span></p> <ul> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">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.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Train every engineer to work AI-natively and redefine the working model — SDLC steps, roles, human-in-the-loop checkpoints, definition of done.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Own a measurable plan to lift productivity 3–5x: baseline throughput and cycle time, instrument each change, report outcomes.</span></li> </ul> <p><span style="color: #0e7c7b;"><strong>4. Raise the Data Product Quality Bar</strong></span></p> <ul> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Enforce engineering standards on every production solution: CI/CD for pipelines, infrastructure as code, data observability, data quality frameworks.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Implement agentic data quality: automated drift detection, root-cause identification, human-in-the-loop recovery.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Meet HIPAA, RAQA, and data classification requirements at design time. If a product does not meet the bar, it does not ship.</span></li> </ul> <p><span style="color: #0e7c7b;"><strong>5. Make Data Self-Service for Humans and AI</strong></span></p> <ul> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Own the NDP contribution and discovery model: what gets published, how it is documented, and how human and AI consumers find and evaluate certified assets.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Ensure every catalog entry carries semantic metadata and AI-readiness classification so business users and AI agents answer their own questions without an engineering ticket.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Partner with the Data Governance Lead to embed data contracts, certification review, and access policy into the lifecycle so self-service is safe and correctly scoped.</span></li> </ul> <p><span style="color: #0e7c7b;"><strong>6. Lead the Team and Set the Technical Bar</strong></span></p> <ul> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Hire, coach, and grow data and analytics engineers; set clear expectations, give direct feedback, build real career paths.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Stay hands-on: review architecture and code, debug production failures, and set the standard by example across Snowflake, AWS, Claude, Sigma, dbt, Fivetran, Python, and Airflow.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Be the engineering face of data products to business stakeholders: translate ambiguous needs into scoped, time-bound commitments; when things change, communicate early with a plan.</span></li> </ul> <h1><span style="color: #0b2545;"><strong>WHAT WE’RE LOOKING FOR</strong></span></h1> <p><span style="color: #0e7c7b;"><strong>Required</strong></span></p> <ul> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">10+ years in data engineering, 5+ leading data or analytics engineering teams at Director level.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Hands-on depth: you write and review Python and SQL, critique dbt models, debug pipeline failures, and make architecture calls yourself.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Shipped data products to production with measurable adoption. You can name the products, who used them, and what changed.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Regulated-environment delivery (healthcare, life sciences, diagnostics, pharma) with PHI and real HIPAA compliance experience.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Modern data stack: Snowflake, AWS, Claude, dbt, Fivetran, Sigma, orchestrator such as Airflow or Dagster; CI/CD for data pipelines and infrastructure as code.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Working knowledge of data mesh and headless, domain-owned data products built for human and AI consumers.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Built or led teams using AI-assisted development in production: agents, code generation, AI-driven testing and validation.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Defined engineering standards, golden paths, or operating models that scaled across multiple teams or domains.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Strong communicator: runs a stakeholder review, writes a technical proposal, represents engineering with executives.</span></li> </ul> <p><span style="color: #0e7c7b;"><strong>Nice to Have</strong></span></p> <ul> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Diagnostics, genomics, or clinical data engineering (BAM, VCF, FASTQ).</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Vector databases, embeddings, or RAG architectures in a data engineering context.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Data contracts as engineering artifacts: schema enforcement, versioning, change management.</span></li> <li style="color: #000000 !important;"><span style="color: #1a1a1a;">Rolled out enterprise agentic tooling (e.g., Claude Code) to an engineering org, including training and change management.</span></li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description">The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years &amp; depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.</div><div class="title">Remote USA</div><div class="pay-range"><span>$186,700</span><span class="divider">&mdash;</span><span>$233,400 USD</span></div></div></div><div class="content-conclusion"><p><strong>OUR OPPORTUNITY</strong></p> <p>Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives.</p> <p>The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.</p> <p><strong>WHAT WE OFFER</strong></p> <p>Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program!</p> <p>For more information, visit <a href="http://www.natera.com/" data-cke-saved-href="http://www.natera.com/">www.natera.com</a>.</p> <p>Natera is proud to be an Equal Opportunity Employer. We are committed to ensuring a diverse and inclusive workplace environment, and welcome people of different backgrounds, experiences, abilities and perspectives. Inclusive collaboration benefits our employees, our community and our patients, and is critical to our mission of changing the management of disease worldwide.</p> <p>All qualified applicants are encouraged to apply, and will be considered without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, age, veteran status, disability or any other legally protected status. We also consider qualified applicants regardless of criminal histories, consistent with applicable laws.</p> <p><em>If you are based in California, we encourage you to read this important information for California residents.&nbsp;</em></p> <p>Link: <a href="https://www.natera.com/notice-of-data-collection-california-residents" target="_blank">https://www.natera.com/notice-of-data-collection-california-residents/</a></p> <p>Please be advised that Natera will reach out to candidates with a @<a href="http://natera.com/" target="_blank" data-saferedirecturl="https://www.google.com/url?q=http://natera.com&amp;source=gmail&amp;ust=1657718972773000&amp;usg=AOvVaw3zRwaIiu7070kJKNG4hjRm">natera.com</a>&nbsp;email domain ONLY. Email communications from all other domain names are not from Natera or its employees and are fraudulent. Natera does not request interviews via text messages and does not ask for personal information until a candidate has engaged with the company and has spoken to a recruiter and the hiring team. Natera takes cyber crimes seriously, and will collaborate with law enforcement authorities to prosecute any related cyber crimes.</p> <p>For more information:<br>- <a href="https://www.bbb.org/article/tips/12261-bbb-tip-employment-scams" target="_blank">BBB announcement on job scams</a>&nbsp;<br>- <a href="https://www.fbi.gov/investigate/cyber" target="_blank">FBI Cyber Crime resource page</a>&nbsp;</p></div>