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Salary: USD 195,000 - 195,000 / annual
CodeRabbit is the leading AI code review platform trusted by 17,000+ customers and 150,000 open-source projects, processing over 2 million code reviews weekly. The company recently closed a $143M Series C at $1.5B valuation and is building Agentic Change Management—the control layer for software changes created by humans and AI.
You will be the founding engineer for the Scaled Success team's data and automation tooling. This team manages the health of a customer base in the tens of thousands using a configuration-driven model of account health, triggering the right motion at the right moment: automated outreach, in-product guidance, or human conversation.
You will build the pipelines, scoring jobs, and services that transform product and business data into customer-facing motions, and the data layer behind internal tools the team uses daily. You'll work directly with a hands-on technical Director who builds the front end. The design work is complete; you start from a written technical spec and working prototypes, with your job being to make the system run reliably in production.
Key responsibilities include: scheduled pipelines from billing, product telemetry, support, and CRM systems into the data warehouse; a nightly scoring job that classifies every account and detects meaningful change; a realtime service that triages account risk events and routes them to automated or human follow-up within minutes; campaign audience syncs with experiment controls; digests and reporting views; and AI workloads where language is the input (classification, extraction, drafting) engineered for cost and reliability.
In your first 90 days, you'll ship the first scheduled pipeline and account scoring job validated against existing reporting, stand up the realtime triage service with alerting and runbooks, and support the first automated campaign cycle with measurement controls.
You 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, taken products from development through production deployment with real users depending on them, and worked directly with customer success tooling (CS platforms, CRM, marketing automation, support systems). You have deep experience with AI: designing workflows, deploying and operating different models in production, and matching the right model to each task. You ship on a weekly cadence, sequence your own work against a written roadmap, and validate and reconcile data before anyone has to ask.
Nice-to-have skills include experience with customer success, revenue, or billing data models; cost and reliability engineering for AI workloads (caching, batching, structured outputs, model routing); and building internal tools that teams use daily.
