<h1><strong>About the Role</strong></h1> <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p> <h1><strong>Key Responsibilities</strong></h1> <h2><strong>Technical Leadership & Architecture</strong></h2> <ul> <li>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.</li> <li>Translate business stakeholder needs into well-scoped technical designs, breaking down complex analytics initiatives into deliverable milestones for the team.</li> <li>Establish and enforce engineering standards for modeling, pipeline design, testing, CI/CD, and observability so solutions are built consistently and reusably across business domains.</li> </ul> <h2><strong>Hands-On Solutions Engineering</strong></h2> <ul> <li>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.</li> <li>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.</li> <li>Develop AI solutions to enable business productivity and automation through use of agentic workflows, RAG/retrieval, and LLM pipelines (extraction/classification)</li> <li>Write production-grade SQL and Python; contribute to shared frameworks, templates, and tooling that let the team deliver new analytics solutions faster.</li> <li>Ensure the data layer supports performant, trustworthy analytics and LLM querying ; lead root-cause analysis on complex data issues and drive durable fixes.</li> </ul> <h2><strong>AI-Native Ways of Working & Mentorship</strong></h2> <ul> <li>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.</li> <li>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.</li> <li>Mentor and grow engineers through code review, pairing, and design coaching; partner with the engineering manager on technical hiring, onboarding, and skills development.</li> </ul> <h2><strong>Data Quality, Reliability & Compliance</strong></h2> <ul> <li>Implement data quality checks, contracts, lineage, and monitoring so that downstream consumers can trust the data by default.</li> <li>Own operational excellence for the team’s solutions: SLAs, incident response, and cost efficiency of compute and storage.</li> <li>Ensure pipelines handling PHI and genomic data meet HIPAA, CLIA, and internal security and governance requirements, including access controls, auditability, and retention.</li> </ul> <h2><strong>Cross-Functional Partnership</strong></h2> <ul> <li>Work directly with business stakeholders across functions to understand their questions, shape analytics solutions to their needs, and communicate technical tradeoffs in plain language.</li> <li>Collaborate with data platforms and AI platform teams to ensure solutions build on shared foundations rather than one-off pipelines.</li> <li>Represent the team in architecture forums, data governance discussions, and tool evaluations.</li> </ul> <h1><strong>Qualifications</strong></h1> <h2><strong>Required</strong></h2> <ul> <li>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.</li> <li>Demonstrated ability to mentor engineers, run effective design and code reviews, drive technical consensus, and communicate clearly with technical and non-technical audie