Lead Bioinformatician/Engineer (Pipelines and Infrastructure)

Natera is seeking a Lead Bioinformatics Engineer to own pipeline and infrastructure engineering for a research informatics team working on cell-free DNA (cfDNA) screening in Women's and Organ Health. This is an individual contributor role.

You will own our WDL workflows, the transition toward Nextflow, the AWS infrastructure the team runs on, and the tooling around them. You will inherit a working system from the person who built it, and with them still on the team to teach you.

Our workflows leave clear wins available in cost and runtime for whoever takes them on. Getting scientists to the point where they process their own samples end to end, without an engineer in the loop, is a major thrust of this role.

Workflow and Pipeline Engineering: Maintain and improve our WDL workflows on AWS Batch and AWS HealthOmics. Own how new Nextflow workflows and existing WDL workflows run on one set of tooling and infrastructure. Make pipelines easier for scientists to run correctly without engineering support.

Infrastructure and Operability: Own the team's AWS infrastructure, managed as code in Terraform. Improve reliability, reproducibility, and observability across our compute environments. Build testing, monitoring, and alerting that surface failures before a scientist finds them.

Tooling and Developer Productivity: Build and harden internal tooling that reduces manual work for the team, including support for triage of production issues. Make new capabilities easier to test, document, and hand off.

Ways of Working: Use coding agents as a routine part of engineering here, for implementation, code review, investigation, and operational triage. You own what you ship regardless of what produced it, which means reviewing and testing agent-written code to the same standard as your own, and helping the team decide which of these tools are worth adopting.

Collaboration and Handoff: Partner with the team's science leads and data scientists on getting new methods into production pipelines and providing first line review of their changes, and with the production organization on handoff readiness for capabilities moving out of research.

• You own the team's pipelines and infrastructure without supervision.
• Transition to new workflows on the same tooling and infrastructure from our existing workflows.
• Scientists run and debug their own pipeline work without coming to you first.
• Operational interruptions to the team's scientific work are down.
• Agent-assisted work is a normal part of how you work.

• Degree in Bioinformatics, Computer Science, Computational Biology, or a related field. A degree is not a requirement: equivalent depth built through work counts fully.
• 4+ years of software or pipeline engineering experience, preferably in a life sciences, sequencing, or production-adjacent setting. We count relevant experience from the point your work became substantially independent, however you got there.
• Demonstrated ownership of a system end to end. We mean operating it in production, not only building the first version.

• Strong Python and software engineering fundamentals, including testing and debugging practice.
• Experience with at least one workflow system: Snakemake, CWL, WDL, Nextflow, or similar.
• Real cloud experience, ideally AWS, including managing infrastructure as code. Terraform is what we use; another tool is fine.
• Solid Linux, container, and version control practice.
• You have built at least one AI-assisted workflow you still use, and you can say what it replaced and how you know it works.
• A track record of picking up unfamiliar tools quickly, more than any specific tool on your resume.
• Clear written communication, including documentation someone else can act on.

• Direct WDL or Nextflow experience.
• Familiarity with AWS Batch or HealthOmics.
• Enough scientific curiosity to understand the biology behind the pipeline and ask useful questions about it.