GRAIL’s Research department is seeking a Staff Data Engineer to lead the design, development, and evolution of data systems that power GRAIL’s product pipeline, from sample collection through processing, analysis, and regulatory submission. This role operates at the intersection of computational science, engineering, and clinical research, enabling high-impact decision-making across the organization.
The Staff Data Engineer is a technical leader who partners with scientists, statisticians, and engineering teams to shape system architecture and deliver robust, analysis-ready datasets. This individual operates with a high degree of autonomy, tackling complex and ambiguous challenges, and influencing cross-functional teams to align on data standards, best practices, and long-term solutions.
They will develop deep expertise in GRAIL’s end-to-end data lifecycle, including EDC, LIMS, Bioinformatics Pipelines, and TidyData, an internally-developed system that aggregates and serves combined datasets. They will lead efforts to improve interoperability, scalability, and data quality across these systems.
The Staff Data Engineer will also collaborate with software engineers and scientists to develop dataset requirements, develop code and procedures to support dataset generation, perform QC, and troubleshoot issues that arise. As needed, the Staff Data Engineer will also contribute to new reporting, data visualization, and statistical analysis features.
Impact & ScopeOwn and drive large, complex data initiatives that impact multiple teams and stages of the product pipeline
Define and evolve data architecture, standards, and best practices across systems
Influence technical direction and strategy for data engineering within Research and partner organizations
Act as a subject matter expert and technical leader, guiding others and elevating team capabilities
Solve ambiguous, high-impact problems requiring deep technical judgment and cross-domain understanding
Responsibilities
Collaborate with data scientists, biostatisticians, and clinical teams to deliver data solutions and sample selections that support clinical trial and research analysis goals
Translate complex scientific and analytical requirements into robust, reusable data solutions
Contribute to data quality frameworks, including standards for validation, reconciliation, and observability across datasets
Drive self-service data platform strategy, implementation and tooling, adoption through training and documentation
Lead efforts to standardize and improve dataset generation, QC, and reporting workflows
Evaluate and introduce new technologies, methodologies, and best practices to improve data management in a regulated biotechnology environment
Mentor other engineers and contribute to technical leadership, standards, and best practices across the organization
These responsibilities summarize the role’s primary responsibilities and are not an exhaustive list. They may change at the company’s discretion
Required Qualifications
BS with 8+ years, MS with 5+ years, or PhD with 3+ years of experience in a computational or scientific field (life science, computer science, engineering, mathematics, statistics, bioinformatics, etc.)
Advanced proficiency in Python or R, with strong software engineering fundamentals
Demonstrated experience designing end-to-end data systems and architectures — from ingestion and transformation to orchestration and visualization
Deep understanding of data modeling, pipelines, orchestration, and data quality practices
Proven ability to lead complex, cross-functional projects with significant business or scientific impact
Strong communication skills, with the ability to influence technical and non-technical stakeholders
Experience operating with high autonomy in ambiguous problem spaces
Preferred Qualifications
Experience with distributed systems or system-level programming (Go, Java, C++)
Familiarity with bioinformatics, clinical data systems, or molecular biology concepts
Experience with cloud platforms (AWS) and modern data infrastructure
Experience driving technical strategy, standards, or platform adoption
Intermediate experience with AI-assisted development workflows
Strong SQL and data warehousing expertise
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