CodeRabbit
Success Analytics Engineer
About this role
CodeRabbit seeks a founding engineer to build data pipelines, scoring systems, and real-time services that power customer success automation for a 17,000+ customer AI code review platform. You'll own the data layer turning product, billing, and support signals into targeted customer motions, working directly with a technical director from spec through production.
What you'll do
- Design and deploy scheduled data pipelines ingesting billing, product telemetry, support, and CRM data into the warehouse
- Build a nightly scoring job that classifies accounts and detects meaningful changes in customer health
- Develop a real-time triage service routing account risk events to automated or human follow-up within minutes
- Implement campaign audience syncs with experiment controls and measurement frameworks
- Engineer AI workflows for classification, extraction, and content generation optimized for cost and reliability
- Create reporting views and internal console data layers supporting the success team's daily operations
What they're looking for
- SQL and data warehouse design (Snowflake, BigQuery, or similar)
- Python backend development and data scripting
- dbt or equivalent data transformation tools
- Scheduled pipeline and webhook-driven service architecture
- Production AI deployment and model routing
- CRM, marketing automation, and customer success platform integration
- Real-time data systems and event-driven architecture
- Experimentation design and statistical controls
Benefits
- Founding engineer role with high autonomy and direct impact on product direction
- Well-funded company ($1.5B valuation, recent $143M Series C)
- Work with a small, senior, results-focused team
- Opportunity to own end-to-end data infrastructure from design to production
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CodeRabbit
CodeRabbit builds an AI-powered code review platform that integrates advanced language models into developer workflows to enhance code quality and productivity. The company is hiring field engineers to support sales and customer success, backend engineers to build scalable infrastructure, and applied AI engineers to design and deploy generative AI systems powering their platform.
- Website
- coderabbit.ai
Likely interview questions
- Walk us through a production data pipeline you built—what data sources did you integrate, and how did you validate correctness before users relied on it?
- Describe your experience deploying and operating AI models in production. How have you approached cost optimization or reliability trade-offs?