Director, Data Product Engineering

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, 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