GTM Data Quality Analyst

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.

THE OPPORTUNITY

You'll own the quality of the data that underpins every routing rule, territory, forecast and ARR number at CodeRabbit. You'll start by working the requests our GTM and Finance teams send to RevOps every day, and you'll turn each kind of request into a written runbook. Then you'll build the Data Quality Agent: an AI agent that handles most of those requests itself, routes the risky ones to a human for approval, and keeps a full record of everything it does. You'll join Business Systems, a small team that builds with agentic tools every day, and you'll work on the same engineering harness our engineers use to ship Salesforce and internal tool changes.

KEY RESPONSIBILITIES

- Own the tier-1 data queue for internal GTM systems data support: account ownership changes, account creation, merges, parent-child hierarchies, lead conversion, and linking billing subscriptions to the right Salesforce account.

- Resolve requests within the team's response standard and leave a record any teammate can pick up.

- Write and maintain a runbook for every recurring request type: the trigger, the checks, the steps, the edge cases, and when to escalate.

- Design, build and run the Data Quality Agent with Code Rabbit Coding Agent and our engineering harness, starting in shadow mode and earning its autonomy one workflow type at a time.

- Define the agent's approval tiers: what it can fix on its own, what it proposes for human approval, and what it always escalates.

- Run duplicate detection and merge cohorts across accounts, contacts and leads, and keep account hierarchies accurate as customers grow, merge and reorganize.

- Close enrichment gaps (firmographics, domains, HQ location, parent company) using our enrichment tools and the data warehouse.

- Track the request queue and data health with clear metrics: volume by request type, time to resolve, agent accuracy, and how often humans override the agent.

- Find the root cause behind repeat requests and bring proposals to the engineers so the fix lands upstream.

QUALIFICATIONS

- 3+ years in CRM data operations, sales operations, RevOps or data quality at a B2B SaaS company.