Key skills
Data EngineeringSnowflakedbtPythonData Pipeline DevelopmentData Quality TestingFailure RecoveryReal-world Clinical DataHealthcare Data StandardsAICustomer Success
About this role
Verantos is the market leader in high-accuracy real-world evidence generation. They are seeking a Senior Data Engineer to build and maintain data pipelines that handle complex, real-world clinical data, ensuring quality and reliability while contributing to quarterly data product releases.
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
Requirements:
- 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
- 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