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Courier Health

Business Intelligence Engineer

New York, New York, United States$120k–$150kfull-timemidAdded today

About this role

Courier Health is seeking a Business Intelligence Engineer to join an early-stage data team building the analytics foundation for a patient engagement platform serving life sciences companies. You'll own data modeling, pipeline reliability, and reporting systems using modern tools like dbt, PostgreSQL, and Sigma.

What you'll do

  • Develop dbt models and SQL transformations with testing to convert raw data into reliable datasets
  • Monitor and improve replication and orchestrated pipeline reliability with data quality checks
  • Build and refine datasets and dashboards for internal teams and client analytics
  • Investigate data issues and implement solutions to strengthen data infrastructure
  • Collaborate with senior engineers through code review and knowledge sharing
  • Support the transformation of data architecture as the platform scales

What they're looking for

  • SQL and relational data modeling
  • Data pipeline development and production environments
  • dbt and transformation layer design
  • PostgreSQL or similar databases
  • CDC/replication and orchestration tools (Airflow, Estuary Flow, Debezium/Kafka)
  • Cloud data warehouse experience (Redshift preferred)
  • BI tool proficiency (Sigma, Looker)
  • Healthcare/HIPAA and PHI-handling awareness

Benefits

  • 100% paid employee health benefits (medical, dental, vision)
  • 401(k) with employer match
  • Unlimited vacation
  • Paid parental leave
  • Commuter benefits
  • Wellness stipend
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Courier Health

Courier Health builds patient engagement platforms for life sciences and biopharma companies, helping them manage chronic disease treatment and communication. The company is hiring Software Engineers, Data Engineers, Customer Engineers, and Customer Value Engineers to develop and scale its platform, support client implementations, and demonstrate customer impact through analytics.

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Likely interview questions

  • Walk us through a complex data transformation project you've built—what was the architecture and how did you ensure data quality?
  • Tell us about a time you discovered a data pipeline issue in production. How did you diagnose and resolve it?
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