[Remote] Director, Data Product Engineering

Note: The job is a remote job and is open to candidates in USA. Natera is a global leader in cell-free DNA testing for oncology, women’s health, and organ health. The Director, Data Product Engineering will lead the team responsible for building, standardizing, and operating domain data products and AI-enabled analytical solutions, while establishing self-service analytics, AI-native engineering practices, and scalable data product standards.

Responsibilities

• 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
• 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
• 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
• 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
• 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
• 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

Skills

• 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
• 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

Benefits

• Comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents.
• Natera employees and their immediate families receive free testing.
• Fertility care benefits.
• Pregnancy and baby bonding leave.
• 401k benefits.
• Commuter benefits.
• A generous employee referral program.

Company Overview

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