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RevenueCat is the default monetization platform for mobile apps, processing $12B+ in annual purchase volume and serving >40% of newly shipped subscription apps. The company is a remote-first team of 150+ spread across 25+ countries, backed by Y Combinator (S18 batch).
You'll join the Analytics team as a Data Analyst, working as a direct partner to business teams including Marketing, Sales, Finance, People, Operations, and Product. The role emphasizes domain knowledge over pure technical skills—understanding subscription mechanics, revenue tracking, refunds, and the nuances of RevenueCat's data model is what makes analysis actionable.
Your primary responsibilities include partnering with business teams to understand their goals and decisions, owning analysis end-to-end from clarifying questions through delivering insights, and building trusted analytics assets (dbt models, LookML explores, dashboards). You'll develop deep expertise in the subscription domain and document it so knowledge scales beyond your head. A key part of the role is using and improving RevenueCat's AI agent tooling for data access—curating semantic context, validating answers, and hardening definitions.
You'll spend most time in Slack and meetings with stakeholders, translating vague business questions into precise analysis and translating data findings into language non-technical teams can act on. You'll also contribute to the data platform itself when it unblocks your work: small model improvements, pipeline debugging, and incident response.
The ideal candidate has 3+ years as a Data Analyst, BI Analyst, Business Analyst, or Analytics Engineer, with direct experience partnering with business teams. You're driven by curiosity—uncomfortable when you don't understand why a number is what it is, asking the question behind the question, and preferring to learn new domains over new tools. You're comfortable with ambiguity and create structure where none exists.
Required skills: strong SQL and warehouse fluency, experience owning datasets and dashboards for non-technical teams, comfort working in version-controlled repos (git, dbt, LookML), daily use of AI agents with healthy skepticism, and clear written communication about limits and caveats. Nice-to-haves include Python, dbt, Looker/LookML, Snowflake/ClickHouse, subscription or fintech domain experience, and high-volume data work.
