Candid Health
BI (Analytics) Engineer
About this role
Candid Health seeks a BI/Analytics Engineer to design and implement data models, pipelines, and infrastructure that support analytics and future AI/ML initiatives in healthcare technology. You'll bridge analytics engineering and business intelligence while scaling the data team's capabilities and establishing best practices.
What you'll do
- Build and own data models and reporting systems for diverse business use cases
- Design and scale data infrastructure to support ML and AI product development
- Develop data pipelines and implement processes for data collection and analysis
- Collaborate with engineers to understand production data and ensure accuracy
- Establish data modeling standards and operational excellence practices
- Maintain and improve visualization and analytics tooling platforms
What they're looking for
- SQL (complex queries in data warehouse environments)
- Data modeling and pipeline design
- Data warehousing (Snowflake, BigQuery, or Redshift)
- Python
- dbt (data build tool)
- Google Cloud Platform and BigQuery
- Metabase or similar visualization tools
- Terraform (infrastructure as code)
Benefits
- Competitive salary range: $135,000–$180,000
- Equity compensation
- Healthcare technology impact
- Minimal hierarchy and collaborative environment
- Opportunity to scale data team and infrastructure
- Strategic role in long-term product development
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Candid Health
Candid Health builds healthcare billing software designed to improve billing operations and customer experiences through scalable, reliable systems. The company is hiring Software Engineers, Data Engineers, Product Security Engineers, Forward Deployed Engineers, and Engineering Leaders to develop its platform infrastructure, data solutions, and customer-facing features.
- Website
- candid.health
Likely interview questions
- Walk us through your experience building and maintaining data models in a production environment. How do you approach designing schemas for different use cases?
- Tell us about a time you designed a data pipeline from scratch. What challenges did you face and how did you solve them?