Sr. Software Engineer/Tech Lead, Data & AI Engineering

Natera is a global leader in cell-free DNA testing serving oncology, women’s health, and organ health. The Data & AI Engineering Tech Lead will set technical direction for analytics and AI solutions, while remaining hands-on in architecture, production engineering, AI-native development practices, mentorship, data quality, compliance, and cross-functional delivery.

Responsibilities:

• 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
• 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
• 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
• 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
• 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

Requirements:

• 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
• 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