Data Engineer 1, Operational Technology - Operations #4941

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. For more information, please visit grail.com Responsibilities: Build and maintain data pipelines that ingest and integrate information from laboratory instruments, automation systems, sequencers, operational platforms, APIs, autonomous robotics platforms, databases and file based data sources. Support downstream analytics, reporting, and AI systems by delivering clean, trustworthy datasets and timely data extracts for troubleshooting, root-cause investigations and platform improvements. Develop and optimize SQL and transformation logic to cleanse, standardize, and model raw instrument and production data into reliable, well structured datasets. Build and support datasets and data models used by operational dashboards, analytics, process monitoring, troubleshooting, and governed AI enabled workflows. Implement orchestration, testing, monitoring and alerting so that data failures, freshness issues, schema changes, and incomplete processing are identified early. Implement data validation and quality checks to ensure datasets are accurate, complete, and reliable. Document pipelines, data models, and datasets to support reproducibility and compliance with ISO, CLIA, CAP, NYS, GMP, and FDA requirements. Continuously improve your technical skills and the team's engineering practices. Required Qualifications: 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 a 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 a commitment to data quality, reliability and accuracy. Ability to collaborate effectively in teams of technical and non-technical individuals, and comfortable working in a rapidly changing environment with dynamic objectives and fast iteration. Ability to investigate technical problems methodically, continuously learn and communicate clearly. A 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.