Senior Data Engineer (Remote, US-Based)

About Verantos

Verantos is the market leader in generating high-accuracy real-world evidence (RWE). Our platform integrates heterogeneous real-world data sources, including electronic health records (EHR), to produce evidence suitable for regulatory and reimbursement use. We leverage data science and artificial intelligence on a heterogeneous AWS-centric tech stack. Our cross-functional teams collaborate to deliver meaningful, real-world impact for leading biopharma companies.

Job Description

The data powering Verantos's evidence platform originates from real-world clinical systems, which are often messy, inconsistent, and constantly evolving. We seek a Senior Data Engineer adept at building robust pipelines that gracefully manage this complexity, rather than constantly battling issues. In this senior role, you will define the technical direction for ingesting, transforming, and quality-checking data at scale, with a focus on self-sustaining systems. A key aspect of this role is understanding the downstream impact of data on researchers and applying that perspective to engineering decisions.

This is a fully remote, US-based position.

Responsibilities

• Lead the design and evolution of the data platform architecture, establishing team-wide patterns and standards.
• Build and operate production-grade data pipelines for ingesting and transforming high-variance, real-world clinical data reliably and at scale.
• Design for automation from the outset, creating pipelines that detect problems, recover gracefully, and surface issues without manual intervention.
• Contribute to quarterly data product releases, collaborating closely with product, clinical, and customer success teams.
• Develop data quality tests that align with the evolving needs of downstream consumers.
• Mentor and elevate other data engineers through code reviews, architecture decisions, and shared standards.
• Actively use and advocate for AI tools to improve team development velocity and code quality.

Qualifications

• 8+ years of experience in data engineering, with demonstrated technical lead experience.
• Production experience with Snowflake and dbt as primary data platform tools.
• Strong Python skills for building and maintaining data pipelines.
• Proven experience building resilient pipelines on irregular, high-variance data sources and maintaining them without constant oversight.
• Systems thinking approach: designs for observability, failure recovery, and automation.
• Ability to engage meaningfully with the business and domain context of the data, beyond just the engineering aspects.
• Active user of AI tools in personal work and curiosity for applying them within pipelines, especially for data quality monitoring and anomaly detection at scale.
• Clear communication skills and ability to work effectively across engineering, product, and clinical stakeholders.

Nice to Have

• Familiarity with OMOP CDM.
• Experience with EHR data or other clinical datasets.
• Familiarity with other healthcare data standards like HL7 or FHIR.
• Experience with data observability tooling in production environments.

Compensation

The base salary range for this position is $150,000–$220,000, depending on experience.