Director, Data Product Engineering

Pharmadog; United States; Hybrid; USD 187,000 - 233,000; Full time

Job summary

Natera is seeking a product engineering leader to design, deliver, and operate domain data products and AI-enabled analytics on the NDP. 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.

The role emphasizes building a self-service analytics experience for business users, creating an operating model for reusable data products, and scaling AI-native practices to

Qualifications

• 10+ 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.
• Strong communicator: runs a stakeholder review, writes a technical proposal, represents engineering with executives.

Responsibilities

• Lead cross-functional data products, analytics experiences and KPIs.
• Create operating model to deliver analytics with embedded data/AI engineers.
• Track and report delivery KPIs: time-to-delivery, certified dataset counts, adoption, incidents.
• Define and enforce publishing standards for analytics products: semantically correct, AI-ready, lineage documented, ownership assigned.
• Establish the headless data product standard: domain-owned assets any authorized consumer can use.
• Build golden paths (templates, examples, documented patterns) for baselines and operating model.

Skills

Python

SQL

Data engineering

Stakeholder communication

Technical leadership

Tools

Snowflake

AWS

Claude

dbt

Fivetran

Sigma

Airflow

Dagster

Job description

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

• Build a self-service analytics product experience for business users and
• Create a catalog of AI ready gold-standard cross functional data products
• 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, human-in-the-loop recovery.
• 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 Self-Service for Humans and AI

• 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.
• 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.
• 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.

6. Lead the Team and Set the Technical Bar

• Hire, coach, and grow data and analytics engineers; set clear expectations, give direct feedback, build real career paths.
• 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.
• 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.

WHAT WE’RE LOOKING FOR

Required

• 10+ 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.
• Strong communicator: runs a stakeholder review, writes a technical proposal, represents engineering with executives.

Nice to Have

• Diagnostics, genomics, or clinical data engineering (BAM, VCF, FASTQ).
• Vector databases, embeddings, or RAG architectures in a data engineering context.
• Data contracts as engineering artifacts: schema enforcement, versioning, change management.
• Rolled out enterprise agentic tooling (e.g., Claude Code) to an engineering org, including training and change management.

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 & depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.
Remote USA
$186,700—$233,400 USD

OUR OPPORTUNITY

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.

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.

WHAT WE OFFER

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!

For more information, visit www.natera.com.

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.

If you are based in California, we encourage you to read this important information for California residents.

Link: https://www.natera.com/notice-of-data-collection-california-residents/

Please be advised that Natera will reach out to candidates with a @natera.com 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.