Senior Data Engineer

You'll join an early and growing data engineering team that's shaping the foundations of our data platform. We're investing in a modern transformation layer, reliable pipelines, and richer in-product analytics for our clients and you'll have real influence over how that gets built.

You'll be one of the first data engineers on our team. This is hands-on, high-ownership work: building our transformation layer, strengthening our analytics pipelines, and creating the data models that power both internal reporting and the analytics our clients see inside the product.

• →Build and strengthen pipelines: improve the reliability and observability of how data flows into our analytics environment.
• →Build the transformation layer: develop dbt models with clean staging/marts layering, turning raw data into trustworthy, well-tested datasets.
• →Power analytics: build the data models behind in-product analytics, QBRs, Customer Success reporting, and the dashboards our clients use.
• →Own quality: put checks in place so data issues surface before they reach clients.
• →Collaborate: work with Product, Client Solutions, and commercial teams to understand what the data needs to support.

• 6+ years of professional data engineering experience
• Architectural depth: you've designed and evolved analytics data platforms, and reason clearly about when added complexity is worth it versus premature.
• Expert SQL and data modeling: you model data well for analytics and can guide others in doing the same.
• Reliable production pipelines: you've built and operated critical production data pipelines and understand their real-world failure modes.
• Technical leadership: you raise the bar for other engineers through architecture, standards, and mentorship.

• Databases: PostgreSQL across our production and analytics environments, with room to grow into a cloud warehouse as we scale
• Ingestion: CDC / streaming replication into analytics (evaluating tools like Estuary Flow and Debezium/Kafka)
• Transformation: building out a version-controlled, dbt-based staging/marts layer with data tests and CI/CD
• Orchestration: scheduled, observable pipeline runs (e.g. Airflow or similar)
• BI & embedded analytics: a modern BI and embedding platform (we use Sigma) for internal reporting and in-product client analytics, with a maturing semantic/metrics layer
• Domain: life sciences / healthcare data; comfort working in HIPAA-aware, PHI-handling environments is a plus