Staff Data Engineer, Trial Library

• We are looking for a data engineer with 8 or more years of experience, with at least a couple of years operating at staff scope or equivalent impact. Beyond tenure, what matters most is whether you have demonstrated the kind of high-impact ownership this role requires
• Strong pipeline engineering: ingestion, transformation, and orchestration of data at meaningful scale, with real attention to data quality and reliability
• Deep SQL and PostgreSQL fluency, including schema design and query performance
• Solid Python and comfort working in a codebase with TypeScript
• Deep AWS experience (Lambda, Fargate, SQS, RDS, and the surrounding ecosystem) and the ability to choose the right service for the right job
• Proven track record of leveraging AI coding tools creatively and effectively to build production systems
• Demonstrated ability to take autonomous ownership, identifying and resolving systemic issues independently
• Startup experience, where you have built from scratch at an early-stage company and treated ambiguity as an opportunity rather than an obstacle
• Strong systems thinking, encompassing backend architecture, APIs, databases, and scalability under real-world constraints
• Clear communication and influence, with the ability to explain trade-offs to both engineers and non-engineers to earn trust through honesty
• Healthcare alignment with a genuine interest in improving clinical trial access and health equity; HIPAA experience a strong plus

Desirable

• We're hiring a Staff Data Engineer to own the pipelines that move patient data through our platform, from EMR systems to the workflows our teams use to identify eligible patients for clinical trials and help them enroll
• This is the center of a major architectural shift for us: moving from narrow, human-assisted data pulls to robust, automated integrations that ingest complete patient records.
• The clean, complete, and trustworthy data you’ll manage powers our AI-assisted patient-trial matching. If healthcare data at HIPAA-grade stakes, messy real-world inputs, and pipelines that genuinely change patient outcomes sound like the right problem space, please keep reading
• You will own the ingestion and transformation pipelines that bring patient records into our platform and shape them into data our matching systems and human experts can trust
• That means designing for reliability in compute-intensive, long-running workflows: the kind of problems where serial processing breaks under load, timeouts become production incidents, and the right architecture (async pipelines, message queues, container-based compute) separates a working feature from a failing one. You'll own data quality end-to-end: schema design, validation, transformation logic, and the monitoring that catches problems before a clinician does
• You'll partner closely with our AI engineers on the data foundations for patient-trial matching and with our product engineers on how ingested data flows into the workflows our users depend on. You'll monitor production, triage issues quickly, and exercise pragmatic judgment by matching technology to business needs

• Our interview process typically includes four stages, followed by reference checks and an offer. Depending on the role, particularly for technical or senior positions, the process may vary slightly and can include a technical assessment, case study, presentation, practical exercise, additional interview conversations, or conversations with members of our leadership team and investors