Braintrust
Data Engineer
About this role
Braintrust seeks an experienced Data Engineer to build the foundational data systems for a fast-growing AI observability platform. This hands-on, solo role involves designing core data models, pipelines, and metrics that connect product usage, customer accounts, and revenue—while collaborating across Engineering, Product, Sales, and Finance to support business operations.
What you'll do
- Design and maintain core data models connecting product usage, accounts, customers, revenue, pipeline, and billing
- Create trusted metrics for activation, retention, expansion, ARR, and usage-based revenue reporting
- Build pipelines integrating product telemetry, CRM, billing, customer success, marketing, and finance systems
- Develop dashboards and self-serve reporting for cross-functional teams
- Partner with Engineering and Product to improve data quality, consistency, and usability
- Identify opportunities for AI agents to automate operational workflows and reporting
What they're looking for
- SQL and data modeling
- Data pipeline orchestration and transformation
- Production-grade code and system design
- GTM and RevOps knowledge
- Product telemetry and event tracking
- AI/ML tools and workflow automation
- Cross-functional communication
- Billing and usage-based pricing systems
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Braintrust
Braintrust builds an AI observability platform that helps developers evaluate, monitor, and troubleshoot AI applications in production. The company is hiring Developer Support Engineers, Solutions Engineers, Data Engineers, Cloud Infrastructure Engineers, and Python engineers to support customer implementations, maintain SDKs and infrastructure, and drive the platform's technical growth.
- Website
- braintrust.com
Likely interview questions
- Walk us through a time you built a data system from scratch as the first or only data hire. How did you balance foundational architecture with urgent business needs?
- Describe your experience modeling and tracking key business metrics across product, sales, and finance systems. How did you ensure data quality and trust across teams?