Data Engineer

Overview Verantos is the market leader in high-accuracy real-world evidence (RWE) generation. The Verantos RWE platform integrates heterogeneous real-world data sources and generates evidence with the accuracy necessary for regulatory and reimbursement use. The platform leverages data science and artificial intelligence, along with advanced data sources such as electronic health records (EHR), to generate RWE capable of supporting complex clinical studies. Some of the largest biopharma companies in the world are Verantos customers. We use a heterogeneous AWS-centric tech stack with data processing, AI, workflows, and analytics. Our teams are cross-functional and collaborative, bringing together engineering, product, design, QA, and clinical domain experts to deliver meaningful, real-world impact. Job Description The 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. Compensation The base salary range for this position is $150,000–$220,000, depending on experience.