About the Role

Natera is a global leader in cell-free DNA testing, serving patients across oncology, women’s health, and organ health. Our Data & AI organization builds the enterprise data platform and AI systems that turn clinical, genomic, and operational data into products that improve patient care and accelerate the business.

We are looking for a Data & AI Engineering Tech Lead to set the technical direction for a solutions team that builds analytics and AI solutions directly for business stakeholders: data and AI pipelines, data models, semantic layers, and the agents, dashboards, and data products that sit on top of them. This is a hands-on, senior individual contributor role: you will write production code, own architecture and design decisions for the solutions your team delivers, and raise the engineering bar through design leadership, code review, and mentorship.

This team is AI-native by design. We expect engineers to use AI coding harnesses and agentic tooling as a core part of how they build, test, document, and operate solutions, and we expect the Tech Lead to model and scale those practices across the team. You will work in a regulated environment where data quality, lineage, and privacy are core requirements of every solution we ship.

The ideal candidate is a strong engineer first, with a product mindset toward business outcomes, the judgment to make pragmatic architectural tradeoffs, and the communication skills to align engineers, product managers, and business stakeholders around them.

Key Responsibilities

Technical Leadership & Architecture

• Own the technical design and architecture for the analytics and AI solutions your team delivers; drive design reviews and make build-vs-buy and technology decisions in partnership with the data platform and architecture teams.
• Translate business stakeholder needs into well-scoped technical designs, breaking down complex analytics initiatives into deliverable milestones for the team.
• Establish and enforce engineering standards for modeling, pipeline design, testing, CI/CD, and observability so solutions are built consistently and reusably across business domains.

Hands-On Solutions Engineering

• Design, build, and maintain scalable data pipelines and transformations on our cloud data platform, sourcing clinical, laboratory, operational, and commercial data to serve business analytics.
• Develop governed data models and semantic layers that power self-service analytics, dashboards, and data products (e.g., Patient 360, Provider 360, Test 360) for business teams.
• Develop AI solutions to enable business productivity and automation through use of agentic workflows, RAG/retrieval, and LLM pipelines (extraction/classification)
• Write production-grade SQL and Python; contribute to shared frameworks, templates, and tooling that let the team deliver new analytics solutions faster.
• Ensure the data layer supports performant, trustworthy analytics and LLM querying ; lead root-cause analysis on complex data issues and drive durable fixes.

AI-Native Ways of Working & Mentorship

• Use AI coding assistants and agentic tools daily across the development lifecycle—design, coding, testing, documentation, and operations—and measurably increase your own and the team’s throughput.
• Define and scale the team’s AI-native engineering practices: prompt and context patterns, reusable agent workflows, AI-assisted testing and review, and the guardrails that keep quality and compliance intact.
• Mentor and grow engineers through code review, pairing, and design coaching; partner with the engineering manager on technical hiring, onboarding, and skills development.

Data Quality, Reliability & Compliance

• Implement data quality checks, contracts, lineage, and monitoring so that downstream consumers can trust the data by default.
• Own operational excellence for the team’s solutions: SLAs, incident response, and cost efficiency of compute and storage.
• Ensure pipelines handling PHI and genomic data meet HIPAA, CLIA, and internal security and governance requirements, including access controls, auditability, and retention.

Cross-Functional Partnership

• Work directly with business stakeholders across functions to understand their questions, shape analytics solutions to their needs, and communicate technical tradeoffs in plain language.
• Collaborate with data platforms and AI platform teams to ensure solutions build on shared foundations rather than one-off pipelines.
• Represent the team in architecture forums, data governance discussions, and tool evaluations.

Qualifications

Required

• 7+ years of data or software engineering experience, including 2+ years leading technical design and delivery for a team or major workstream, ideally building analytics solutions for business users.
• Demonstrated ability to mentor engineers, run effective design and code reviews, drive technical consensus, and communicate clearly with technical and non-technical audiences.
• Demonstrated, hands-on use of AI coding assistants and agentic tools in daily engineering work, with a point of view on how to use them well.
• Expert-level SQL and strong Python, with a track record of shipping and operating production data pipelines and data models at scale.
• Deep experience with a modern cloud data warehouse and cloud infrastructure (Snowflake/AWS preferred).
• Hands-on experience with transformation frameworks (dbt or equivalent) and workflow orchestration (Airflow, Dagster, Prefect, or similar).
• 1-2 years of practical background building with LLM frameworks (LangChain, LangGraph, or equivalent) alongside managed AI services and model routing gateways like Snowflake Cortex, AWS Bedrock, and foundational APIs.
• Strong grounding in analytical data modeling (dimensional or domain-driven), semantic layers, data quality engineering, and CI/CD for data systems.

Preferred

• Experience in healthcare, life sciences, or another regulated domain handling PHI or other sensitive data.
• Experience with lab data management systems, lab informatics systems, bioinformatics pipelines is a plus
• Snowflake experience, including performance tuning, cost management, and features such as dynamic tables, Snowpark, or Cortex.
• Sigma experience, or experience with comparable BI and semantic-layer tools (Looker, Tableau, Power BI), and an understanding of how to design data models for self-service consumption.
• Experience building and operating agentic or LLM-based workflows within the data development lifecycle.
• Experience with infrastructure-as-code (Terraform) and data observability tooling.
• Experience integrating with Salesforce, laboratory information systems, or EHR data sources.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related quantitative field, or equivalent practical experience.