Verantos
Senior Data Engineer
Remote Data Engineering role with clear candidate location fit.
PostedJul 5, 2026
Eligible countries1 accepted country
Seniority signalSenior
Work settingRemote
Accepted candidate locations
USA
Role overview
Senior Data Engineer
Requirements and responsibilities
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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 haves
- 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.
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