Data Engineer # 4885

GRAIL•Durham, NC

2d•$86,000 - $106,000•Hybrid

About The Position

GRAIL is a pioneering healthcare company focused on advancing early cancer detection through innovative technologies. We are building a multi-disciplinary team of scientists, engineers, and physicians to leverage next-generation sequencing (NGS), large-scale clinical studies, and advanced computer and data science to combat cancer. With headquarters in the Bay Area, GRAIL also has offices in Washington D.C., North Carolina, and the United Kingdom, supported by leading investors and industry partners. This role is for a Senior Data Analytics Engineer in Operational Intelligence, responsible for transforming raw operational and laboratory data into BI-ready datasets and scalable data products. The position involves close collaboration with Software, Operations, Research, Development, and Commercial teams to build and maintain a robust data ecosystem. Key activities include performing analysis, building and maintaining dashboards, and developing analytical tools to monitor high-throughput lab processes. This hands-on role is critical for ensuring data accuracy, accessibility, and utility in lab efficiency, traceability, and product quality, driving business value in a fast-paced environment. The role is based in Durham, North Carolina, and offers a flexible work arrangement with a requirement for at least 60% (24 hours) of the work week to be on-site.

Requirements

Bachelor’s degree in Computer Science, Statistics, Informatics, Information Systems, Engineering, Data Science, Life Sciences, or a related quantitative field.
5+ years of relevant experience as a data analyst, analytics engineer, or data engineer delivering production-grade datasets, dashboards, and reports.
3+ years of data modeling experience (dimensional/semantic).
2+ years building dashboards and reports in a modern BI platform (e.g., Tableau).
Proficiency in SQL and at least one programming language (Python or R).
Hands-on experience with AWS analytics services (e.g., Redshift, S3, Glue, Managed Airflow) and/or Snowflake.
Experience working with SaaS application data sources (e.g., NetSuite, Salesforce, Workday, Coupa).
Proven expertise in process analytics, SPC, statistical methods, and root-cause analysis (e.g., JMP, Minitab; SQL; Python/R).
Demonstrated ability to collaborate with stakeholders and system users to deliver robust solutions and measurable results.
Experience in regulated life sciences/biotech environments and familiarity with clinical laboratory operations.

Nice To Haves

5+ years of laboratory operations experience in regulated medical device, diagnostics, or biotechnology settings.
Master’s preferred.
Experience deploying and scaling process monitoring in high-throughput or automated environments.
Knowledge of end-to-end clinical lab workflows (sample shipment; pre-analytical, analytical, and post-analytical phases).
Strong understanding of regulatory and QMS frameworks (GMP, ISO 13485, ISO 15189, CLIA, CAP, NYS, FDA).
Excellent leadership, communication, and cross-functional collaboration skills

Responsibilities

Build, evolve, and support dashboards, reports, and real-time monitoring tools for functional teams and executives that clearly communicate findings and drive data-informed decisions; enable self-serve analytics and automate common ad hoc requests.
Implement and continuously improve process monitoring systems (e.g., SPC, automated controls, alerting) to track and stabilize process health.
Define, govern, and report KPIs for throughput, quality, efficiency, and compliance; proactively surface trends, anomalies, and risks.
Develop and deploy predictive analytics, trend analyses, and models to anticipate process, equipment, and quality issues; partner with Data Science on model integration and deployment.
Collect, cleanse, and analyze process and production data from high-throughput lab environments; ensure timely, reliable access to accurate datasets.
Provide rapid data extracts and tailored analyses to expedite troubleshooting, containment, and root-cause investigations.
Ensure data quality and integrity through robust testing, validation, lineage, and observability; establish and monitor SLAs for critical datasets.
Maintain comprehensive process documentation compliant with ISO, CLIA, CAP, NYS, GMP, and FDA requirements; ensure monitoring and reporting systems are audit-ready.