Senior Staff Data Engineer

GRAIL, Inc.

United States

United States

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About

At GRAIL, we are on a mission to transform early cancer detection and change the trajectory of cancer mortality. By fostering collaboration among scientists, engineers, and physicians, we utilize next-generation sequencing, population-scale clinical studies, and cutting-edge data science to tackle one of the most pressing challenges in medicine. Based in Menlo Park, California, with additional locations in Washington, D.C., North Carolina, and the United Kingdom, we are pioneering innovative technologies that promise to revolutionize cancer care. Our advanced laboratories process patient blood samples to detect cancer, and our engineering teams are dedicated to developing software and hardware solutions that automate and enhance lab operations. We are currently expanding our team and seeking a highly skilled Senior Staff Data Engineer to join us in this exciting journey. In this hybrid role, you will contribute significantly to building robust data infrastructure, automating workflows, and providing actionable insights to optimize operations. Your expertise will make a direct impact on our ability to deliver effective cancer detection solutions. Key Responsibilities
Collaborate with cross-functional teams to design, deploy, and automate scalable data infrastructure and tools, minimizing manual efforts and enhancing operational efficiency. Model GRAIL data following the FAIR principles (Findable, Accessible, Interoperable, and Reusable) to ensure high data quality and usability. Drive the design and automated delivery of standardized data models, datasets, and tools, integrating effective testing and release processes for software and data deliveries. Work closely with teams across Research, Clinical Lab Operations, and Software Engineering to provide holistic data solutions from inception to delivery. Maintain high standards of quality, clinical compliance, and privacy for all software and data. Mentor junior engineers and scientists, advocating for best practices in data engineering. Preferred Qualifications
B.S. or M.S. in a quantitative field such as Computer Science, Engineering, Mathematics, Physics, or Computational Biology, with a minimum of 8 years of industry experience, or Ph.D. with at least 5 years of relevant experience. Proficient in relational databases, data modeling principles, data pipeline tools, and workflow engines (e.g., SQL, DBT, Apache Airflow, AWS Glue, Spark). Experienced with DevOps practices such as CI/CD pipelines, containerized deployment (e.g., Kubernetes), and infrastructure-as-code (e.g., Terraform). Familiar with supporting data science and machine learning pipelines, especially in biological data analysis using Python. Strong ability to navigate uncertainty and collaborate effectively with product teams and stakeholders to clarify objectives. Excellent written and verbal communication skills, adaptable to diverse audiences. Join us in our remarkable journey to enhance cancer care through innovation. We are based in Menlo Park, CA, but welcome candidates from various locations to contribute to our mission.

United States

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English

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