Job DescriptionThe data that powers Verantos's evidence platform comes from real-world clinical systems - messy, inconsistent, and constantly changing. We need a Senior Data Engineer who knows how to build pipelines that handle that chaos gracefully, not one who fights fires every time something unexpected arrives.
This is a senior role on the team responsible for shipping our data product every quarter. You will set the technical direction for how we ingest, transform, and quality-check data at scale, with an eye toward systems that run themselves. Just as important is the ability to think beyond the pipeline: the best candidate understands what the data means to the researchers who depend on it, and brings that perspective into the engineering decisions they make.
This is a fully remote, US-based role.
Responsibilities- Lead the design and evolution of the data platform architecture, establishing patterns and standards the team builds on.
- Build and operate production-grade data pipelines that ingest and transform high-variance, real-world clinical data reliably and at scale.
- Design for automation from the start: pipelines that detect problems, recover gracefully, and surface issues without requiring manual intervention to run.
- Contribute to quarterly data product releases, working closely with product, clinical, customer success teams to meet commitments.
- Build data quality tests that reflect the evolving needs of our downstream consumers.
- Mentor and elevate other data engineers through code review, architecture decisions, and shared standards.
- Actively use and advocate for AI tools that improve the team's development velocity and code quality.
Qualifications- 8+ years in data engineering, with experience at a technical lead level.
- Production experience with Snowflake and dbt as primary data platform tools.
- Strong Python skills for building and maintaining data pipelines.
- Has built resilient pipelines on irregular, high-variance data sources and knows what it takes to keep them running without babysitting.
- Thinks in systems: designs for observability, failure recovery, and automation.
- Can engage meaningfully with the business and domain context around the data, not just the engineering.
- Uses AI tools actively in their own work and is curious about applying them within the pipeline, particularly for data quality monitoring and anomaly detection at scale.
- Communicates clearly and works well across engineering, product, and clinical stakeholders.
Nice to Have- Familiarity with OMOP CDM - not required, but it matters here more than most places.
- Experience with EHR data or other clinical datasets.
- Familiarity with other healthcare data standards such as HL7 or FHIR.
- Experience with data observability tooling in production environments.
CompensationThe base salary range for this position is $150,000-$220,000, depending on experience.
