Sr. Software Engineer/Tech Lead, Data & AI Engineering
AI in this 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 ResponsibilitiesTechnical 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.
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.
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.
All qualified applicants are encouraged to apply, and will be considered without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, age, veteran status, disability or any other legally protected status. We also consider qualified applicants regardless of criminal histories, consistent with applicable laws.
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.
For more information:
- BBB announcement on job scams
- FBI Cyber Crime resource page
How we rate this
Sr. Software Engineer/Tech Lead, Data & AI Engineering at Natera rates 63 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- What are the limits of LangChain that you've run into, and how did you work around them?
- What's a project where you used LangGraph hands-on?
- Walk me through how you've used Bedrock in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: RAG, AI Agents, LangChain, LangGraph, and Bedrock. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.
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