Hybrid Staff Data Engineer

at GRAIL

• Job title: Staff Data Engineer
• Job type: Full Time
• Experience level: Lead
• Salary: $163,000 - $216,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Airflow, Apache, AWS, Kubernetes, Python, Spark, SQL, Terraform
• Location requirements: Hybrid, Menlo Park, United States

About the role

• Staff Data Engineer designing automated data infrastructure for early cancer detection technologies at GRAIL. Collaborating with engineers and scientists to streamline operations and ensure data quality.

Responsibilities

• Be a part of a highly collaborative team focused on delivering value to cross-functional partners by designing, deploying, and automating secure, efficient, and scalable data infrastructure and tools—reducing manual efforts and streamlining operations.
• Help model Grail data to ensure it follows FAIR principles (Findable, Accessible, Interoperable, and Reusable).
• Drive the design, deployment, and automated delivery of data infrastructure, standardized data models, datasets, and tools.
• Integrate automated testing and release processes to improve the quality and velocity of software and data deliveries.
• Collaborate with cross-functional teams—ranging from Research to Clinical Lab Operations to Software Engineering—to provide comprehensive data solutions from conception to delivery.
• Ensure all software and data meet high standards for quality, clinical compliance, and privacy.
• Mentor fellow engineers and scientists, promoting best practices in software and data engineering.

Requirements

• B.S. / M.S. in a quantitative field (e.g., Computer Science, Engineering, Mathematics, Physics, Computational Biology) with 8+ years of related industry experience, or Ph.D. with at least 5 years of related industry experience or equivalent.
• Extensive experience developing with relational databases, data modeling principles, data pipeline tools, and workflow engines (e.g., SQL, DBT, Apache Airflow, AWS Glue, Spark).
• Extensive experience with DevOps practices, including CI/CD pipelines, containerized deployment (e.g., Kubernetes), and infrastructure-as-code (e.g., Terraform).
• Experience supporting data science and machine learning data pipelines, preferably in the context of biological data analysis and using Python.
• Ability to embrace uncertainty, navigate ambiguity, and collaborate with product teams and stakeholders to refine requirements and drive toward clear engineering objectives and designs.
• You are a strong written and verbal communicator and can adapt your communication style and the level of detail to your audience.

Benefits

• flexible time-off
• 401k with a company match
• medical plans
• dental plans
• vision plans
• mindfulness offerings