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# Business Intelligence Engineer

- **Company:** Courier Health, Inc.
- **Location:** New York, United States
- **Experience:** Experienced
- **Salary:** $120,000.0 - $150,000.0
- **Contract:** Permanent contract
- **Skills:** Information Engineering, Data Transformation, Relational Databases, Raw Data, Standard Sql, SQL Databases, Tableau (Software), Looker Analytics
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/business-intelligence-engineer-courier-health-9631577

## About the Role

* 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

## Description

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.


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