We're looking for a Data Analyst to join our growing data team and help make analytics a core part of how we build. You'll work on the product-usage side of an AI product that thousands of developers rely on — understanding how teams adopt AI code review, which features drive value, and where we can make the experience better. It's a product-focused role at heart, reaching into business, operational, and cost metrics too, with room to shape how we work as the team grows.
Be the go-to analyst for the product team — drive product analytics day to day: how developers use our AI features, plus retention and conversion funnels
Turn raw product and usage data into clear, consistent metrics the team can trust
Build and maintain the modeled data layer, so metrics live in one place and analysis is repeatable rather than rebuilt each time
Build and maintain dashboards so teams can self-serve answers
Run deep-dive analysis to surface opportunities and support product decisions and experimentation
Extend into business and operational KPIs — conversion, churn, segmentation, customer value — and cost / unit-economics
Take analyses from problem definition through to clear recommendations, not just reports
Requirements:
3+ years of industry experience as a Data, Product, Business, or BI Analyst, ideally in a developer-focused or technical B2B product
AI-native: AI tools are a default part of how you work, and you build prompts, scripts, and small agents to automate the repetitive parts of your own analysis workflow
Strong SQL and data modeling: you can move around a data warehouse independently, spot when a query that runs is still returning the wrong answer, and build clean, well-structured datasets that other people can rely on
Experience with a product analytics tool (e.g. Mixpanel) or a BI tool, with proven experience building self-serve dashboards
Strong communication and storytelling — you turn analysis into clear recommendations for non-technical stakeholders
Solid business sense — comfortable moving between product metrics and broader business and operational KPIs
Self-directed and comfortable with ambiguity — you can take a question from definition through to a clear answer with limited guidance, while working closely with a small team
Fluent English, written and spoken
Startup DNA — comfortable with fast iteration, shifting priorities, and wearing multiple hats
