Senior Data Analytics Engineer # 4527

GRAIL•Durham, NC

1d•Hybrid

About The Position

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. As a Senior Data Analytics Engineer in Operational Intelligence, you will bridge Data Engineering, Data Science, and Analytics to curate and transform raw operational and laboratory data into BI-ready datasets and scalable data products. You will collaborate across Software, Operations, Research, Development, and Commercial to design and sustain a robust data ecosystem, while performing analysis and building and maintaining dashboards and analytical tools that monitor high-throughput lab processes. In this strategic, hands-on role, you will ensure the accuracy, accessibility, and utility of data and reporting systems that are critical to lab efficiency, traceability, and product quality, delivering trusted insights that drive measurable business value in a rapidly growing environment. This role is based at our Durham, North Carolina, office. 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 60% requirement for the 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

Master’s preferred.
5+ years of laboratory operations experience in regulated medical device, diagnostics, or biotechnology settings.
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.