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Courier Health

Data Engineer

New York, New York, United StatesmidAdded 1 month ago

About this role

Courier Health is seeking an early-stage Data Engineer to build and strengthen the data platform supporting patient engagement in healthcare. You'll develop dbt models, improve pipeline reliability, and create datasets powering both internal reporting and client-facing analytics.

What you'll do

  • Develop dbt models and SQL transformations with testing to convert raw data into reliable datasets
  • Monitor and improve reliability of replication and orchestrated pipelines with quality checks
  • Build and maintain datasets and dashboards for internal teams and client analytics
  • Investigate data issues and implement solutions to strengthen pipeline performance
  • Participate in code reviews and collaborate with senior engineers on platform improvements
  • Support analytics infrastructure across PostgreSQL and cloud data warehouse environments

What they're looking for

  • SQL and relational data modeling
  • Data pipeline development and production environments
  • dbt (nice to have)
  • Change Data Capture (CDC) or replication tools
  • Orchestration tools like Airflow
  • PostgreSQL or similar databases
  • Cloud data warehouse experience
  • BI tools like Sigma or Looker

Benefits

  • 100% paid health benefits (medical, dental, vision)
  • 401(k) with employer match
  • Unlimited vacation
  • Paid parental leave
  • Wellness stipend
  • Commuter benefits and catered Friday lunches
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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.

View all jobs at Courier Health

Likely interview questions

  • Walk us through a data pipeline you've built in production—what tools did you use, and how did you ensure data quality?
  • Tell us about a time you had to debug a data issue in a pipeline. How did you identify the root cause and prevent it from happening again?
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