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# Senior Data Engineer

- **Company:** Courier Health, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $175,000.0 - $225,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Continuous Integration, Information Engineering, PostgreSQL, Operational Databases, Raw Data, SQL Databases, Data Streaming, Debezium, Apache Kafka
- **Published:** June 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=b91d5930ffe584c8

## About the Role

Do you have experience in Technical architecture?, * 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.

## Description

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., * 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


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