Data Software Engineer; Menlo Park, CA

Our mission is to detect cancer early, when it can be cured. We are working to change the trajectory of cancer mortality and bring stakeholders together to adopt innovative, safe, and effective technologies that can transform cancer care.

We are a healthcare company, pioneering new technologies to advance early cancer detection. We have built a multi-disciplinary organization of scientists, engineers, and physicians and we are using the power of next-generation sequencing (NGS), population‑scale clinical studies, and state‑of‑the‑art computer science and data science to overcome one of medicine’s greatest challenges.

GRAIL is headquartered in the bay area of California, with locations in Washington, D.C., North Carolina, and the United Kingdom. It is supported by leading global investors and pharmaceutical, technology, and healthcare companies.

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We design, optimize, and manage GRAIL’s end‑to‑end data lifecycle, from sample ingestion through analysis, ensuring every step meets rigorous regulatory and clinical standards. We are seeking a Staff Software Engineer to help deliver the high‑quality datasets that power research, analytics, and scientific insights. In this role, you will collaborate with cross‑functional teams to deliver scalable data solutions aligned with FDA and global health authority requirements.

Our ideal candidate brings experience in the life sciences and a strong background in data processing, infrastructure and operational excellence.

This role is based in Menlo Park, California, and will move to Sunnyvale, California in Fall 2026. It offers a flexible work arrangement, with the ability to work from GRAIL's office or from home. Our current flexible work arrangement policy requires that a minimum of 60%, or 24 hours, of your total work week be on‑site. Your specific schedule, determined in collaboration with your manager, will align with team and business needs and could exceed the 40% requirement for the site.

At our Menlo Park campus, Tuesdays and Thursdays are the key days where we encourage on‑site presence to engage in events and on‑site activities.

• Contribute to a highly collaborative team focused on delivering value to cross‑functional partners by implementing processes and tools that prepare analysis‑ready datasets.
• Integrate inputs from diverse data sources across the organization into well‑structured, harmonized datasets.
• Build and maintain access controls and data‑delivery mechanisms that uphold stringent blinding and privacy standards.
• Partner with Research, Clinical Lab Operations, and Software Engineering teams to define and implement data transformations that support downstream analysis.
• Ensure all delivered data adheres to software quality expectations, clinical compliance requirements, sequestration protocols, and privacy regulations.
• Implement automated testing and release processes to improve the reliability, quality, and velocity of software and data deliveries.
• Mentor engineers and scientists, promoting best practices in software development, data engineering, and operational rigor.
• These responsibilities summarize the role’s primary responsibilities and are not an exhaustive list. They may change at the company’s discretion.

• BS/MS/PhD in a quantitative field (e.g., Computer Science, Engineering, Mathematics, Statistics, Bioinformatics) or equivalent and 6+ years of experience in data engineering, ideally within a regulated biotechnology, pharmaceutical, medical device, or healthcare environment.
• Familiarity with comp bio, bioinformatics strongly preferred
• Strong analytical and problem‑solving skills, with a track record of maintaining data quality and integrity across complex datasets.
• Expertise in Go or Python and proficiency in SQL. Experience with R would be nice to have. (we do code assessments thru out the interview process)
• Knowledge of ETL/ELT workflows, data pipeline development, with demonstrated success delivering data solutions for clinical or regulatory use cases. Hands‑on experience with relational databases, data modeling, data pipeline tools, and workflow engines (e.g., SQL, dbt,…