Business Intelligence 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 the owner of business intelligence at Courier Health, driving how internal teams and clients see and understand their data. This is hands-on, high-ownership work: building and maintaining our Sigma dashboards, developing the dbt models that power them, and partnering closely with stakeholders to turn data into decisions.

• →Own our BI layer: design, build, and maintain Sigma dashboards and workbooks that our end-users rely on for embedded in-product reporting and analytics.
• →Build the models underneath: develop dbt models and SQL transformations, backed by tests, that turn raw data into reliable, well-documented datasets purpose-built for reporting.
• →Partner with stakeholders: work directly with internal teams and customer-facing partners to scope reporting needs, define metrics, and translate ambiguous questions into clear, trustworthy dashboards.
• →Shape our semantic layer: define and maintain consistent metrics, naming conventions, and a shared semantic/metrics layer so numbers mean the same thing everywhere they appear.
• →Improve pipeline reliability: help monitor replication and orchestrated pipelines, investigate data issues, and add quality checks upstream of the datasets you report on.

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 experience in business intelligence, analytics engineering, or data engineering
• Strong SQL: you write correct, readable, and performant SQL and have a strong understanding of relational data modeling
• BI tool experience: you’ve built dashboards and reports in a modern BI tool (e.g. Sigma, Looker, Tableau, Mode) that stakeholders actually use and trust. Bonus points if those dashboards were deployed in production systems or a user-facing application.
• Data modeling experience: you’ve developed data transformations (ideally in dbt) in production environments
• Stakeholder instincts: you can work with non-technical stakeholders to figure out what they actually need, not just what they asked for
• Eagerness to learn: you take feedback well, ask good questions, and are excited to grow as an engineer