Success Analytics Engineer
About CodeRabbit
CodeRabbit is the leading AI code review platform, trusted by more than 17,000 customers and 150,000 open-source projects, conducting over 2 million code reviews each week. We build the symbiotic partnership between developers and AI that makes shipping fast software safe again, reviewing every pull request, IDE change, and CLI commit so teams can move quickly without breaking things.
We are a fast-moving, well-funded company, fresh off a $143M Series C at a $1.5B valuation - building Agentic Change Management, the control layer for software changes created by humans and agents. As AI writes more of the world's code, the bottleneck moves from implementation to judgment and helping human judgment scale is exactly the problem we exist to solve.
About the team
CodeRabbit is the AI code review platform. The Scaled Success team is responsible for the health of a customer base in the tens of thousands. We run a configuration-driven model of account health and trigger the right motion at the right moment: automated outreach, in-product guidance, or a human conversation. The team is small, senior, and measured on results.
The role
You are the founding engineer for the team's data and automation tooling. You build the pipelines, scoring jobs, and services that turn product and business data into customer-facing motions, and the data layer behind the internal tools the team works from. You work directly with the Director, who is hands-on technical and builds the front end. The design work is complete: you start from a written technical spec and working prototypes, and your job is to make the system run in production.
What you'll build
Scheduled pipelines from billing, product telemetry, support, and CRM systems into the company's data warehouse
A nightly scoring job that classifies every account and detects meaningful change
A realtime service that triages account risk events and routes each one to automated or human follow-up within minutes
Campaign audience syncs with experiment controls built in
Digests, reporting views, and the data layer behind the team's internal console
AI workloads where language is the input: classification, extraction, and drafting, engineered for cost and reliability
Your first 90 days
Ship the first scheduled pipeline and the account scoring job, validated against existing reporting
Stand up the realtime triage service with alerting and runbooks
Support the first automated campaign cycle running on your data, with measurement controls in place
What you'll bring
4+ years in data, analytics, or backend engineering, with production data warehouse experience (SQL, dbt or similar, Python)
You have built and operated scheduled pipelines and webhook-driven services in production
You have taken a product from development through production deployment and operated it live, with real users depending on it
You have worked directly with customer success tooling: CS platforms, CRM, marketing automation, or support systems, as a builder or a heavy operator
Deep experience with AI: designing AI workflows, deploying and operating different models in production, and matching the right model to each task
You ship on a weekly cadence and can sequence your own work against a written roadmap
You validate and reconcile your data before anyone has to ask
Nice to have
Experience with customer success, revenue, or billing data models
Cost and reliability engineering for AI workloads: caching, batching, structured outputs, and model routing
You have built internal tools that a team uses daily
How we work
The team runs on measured results. Campaigns ship with control groups, numbers are labeled by source, and every report traces to rules anyone can audit.
OTE for this role is up to $195K. Actual salary will be based on job-related skills, experience, and location.