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 data models: develop dbt models and SQL transformations, backed by tests, that turn raw data into reliable datasets.
• →Improve pipeline reliability: help monitor our replication and orchestrated pipelines, investigate data issues, and add quality checks.
• →Power reporting: build and refine the datasets and dashboards that internal teams and clients rely on.
• →Learn and grow: work closely with senior engineers, take part in code review, and steadily take on more ownership.

We're open on exact tools — we care about fundamentals and trajectory. You'll likely have most of the following:

• 3-5 years of professional data engineering experience
• Strong SQL: you write correct, readable SQL and understand relational data modeling
• Data experience: you've developed data pipelines and transformations in production environments
• Eagerness to learn: you take feedback well, ask good questions, and are excited to grow as a data engineer

Nice to have: exposure to dbt, CDC / replication or orchestration tools (e.g. Estuary Flow, Debezium/Kafka, Airflow), a cloud data warehouse (ex. Redshift), a BI tool (ex. Sigma, Looker); comfort with PostgreSQL or a similar database.

• Databases: PostgreSQL across our production and analytics environments, with room to grow into a cloud warehouse (ex. Redshift) 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