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Salary: USD 86,000 - 106,000 / annual
GRAIL is a healthcare company pioneering early cancer detection using next-generation sequencing, population-scale clinical studies, and advanced data science. The company is headquartered in the Bay Area with locations in Washington D.C., North Carolina, and the United Kingdom.
As a Data Engineer on the Operational Technology team, you will build and maintain data pipelines connecting GRAIL's lab instruments, automation systems, and operational platforms to a trusted, well-modeled data foundation. You will own end-to-end ingestion and transformation pipelines, partnering with systems engineers, lab operations, data scientists, and automation engineers to ensure reliable data flow from the lab floor to analytics and AI systems. This is a hands-on role in a fast-paced, regulated environment where you will take ownership of real data infrastructure and grow quickly alongside a talented, highly motivated team.
Key responsibilities include:
- Building and maintaining data pipelines that ingest and integrate information from laboratory instruments, automation systems, sequencers, operational platforms, APIs, autonomous robotics, databases, and file-based data sources
- Supporting downstream analytics, reporting, and AI systems by delivering clean, trustworthy datasets and timely data extracts for troubleshooting and platform improvements
- Developing and optimizing SQL and transformation logic to cleanse, standardize, and model raw instrument and production data into reliable, well-structured datasets
- Building and supporting datasets and data models used by operational dashboards, analytics, process monitoring, troubleshooting, and governed AI-enabled workflows
- Implementing orchestration, testing, monitoring, and alerting to identify data failures, freshness issues, schema changes, and incomplete processing early
- Implementing data validation and quality checks to ensure datasets are accurate, complete, and reliable
- Documenting pipelines, data models, and datasets to support reproducibility and compliance with ISO, CLIA, CAP, NYS, GMP, and FDA requirements
- Continuously improving technical skills and team engineering practices
The role is based on-site in Research Triangle Park, North Carolina, Monday through Friday. The position participates in an on-call rotation and may occasionally require weekend or holiday support for production incidents, maintenance, or critical deployments.
REQUIREMENTS:
- Degree in Computer Science, Mathematics, Software Engineering, Data Science, Life Sciences, Physics, or similar field
- 1+ years of relevant professional, internship, academic, or project experience in data engineering, analytics engineering, software development, or related field, or equivalent practical experience
- Proficiency in SQL
- Working proficiency with one or more programming languages such as Python, Rust, C++, or similar
- Basic understanding of ETL or ELT pipelines, relational databases, and structured or semi-structured data
- Strong attention to detail and commitment to data quality, reliability, and accuracy
- Ability to collaborate effectively in teams of technical and non-technical individuals, comfortable working in rapidly changing environment with dynamic objectives and fast iteration
- Ability to investigate technical problems methodically, continuously learn, and communicate clearly
- Highly analytical mindset and eagerness to solve technical problems
PREFERRED QUALIFICATIONS:
- Familiarity with data pipeline orchestration and transformation tools such as Airflow, dbt, or comparable technologies
- Familiarity with cloud data platforms, object storage, and warehouses such as AWS S3, Redshift, Glue, Snowflake, or comparable technologies
- Familiarity integrating AI/agentic tooling into the data engineering SDLC
- Experience with semantic data modeling, data lineage, and automated data quality testing
- Familiarity with statistical methods or basic process analytics
- Exposure to manufacturing, clinical laboratory operations, diagnostics, or biotechnology
- Experience with version control systems such as Git and collaborative development practices
- Basic understanding of APIs, file transfers, networking, and system integrations
