Data Analyst, RevenueCat

• 3+ years in an analytics role (Data Analyst, BI Analyst, Business Analyst, Analytics Engineer or similar), including real experience as the direct analytics partner to a business team such as Marketing, Sales or Finance
• You're uncomfortable when you don't understand why a number is what it is, and you dig until you do
• You ask the question behind the question. When someone asks for a dashboard, you find out what decision it's for
• You'd rather learn a new domain than a new tool
• You're comfortable without fully formed requirements, and you create structure where none exists yet
• You care more about being useful and clear than about polished dashboards
• You want to be a partner to the business, not a request queue
• Strong SQL and real comfort working directly in a warehouse. You can get to an answer without hand-holding
• Experience owning datasets and dashboards that non-technical teams depend on
• Comfortable working in a repo: git, branches, pull requests, code review. Our analytics lives in version-controlled dbt and LookML repos, not in saved queries
• You already work with AI agents daily and you're appropriately skeptical of them. You can explain how you verified an answer, not just how you produced one
• Clear written communication, especially about limits, caveats, and what a number does not say

Desirable

• We're hiring a Data Analyst to work as close as possible to the teams that run RevenueCat's business, including Marketing, Sales, Finance, People, Ops, Product, etc
• The most valuable thing on our Analytics team today isn't SQL, it's domain knowledge. Knowing what a trial start actually counts, why tracked revenue and realized revenue are different, how store refunds land in our data, and which model answers a question correctly the first time. That knowledge is what turns a half-formed Slack question into a number someone can act on within the hour
• So you'll spend most of your time with business teams: understanding what they're trying to decide, turning vague questions into analysis, and shipping the datasets and dashboards they rely on. You'll build that domain knowledge fast, working day to day with the person who currently owns Analytics here. He'll be your closest partner and the person who helps you grow into the domain
• The second thing that makes this role exciting is how we expect you to work. We're building the infrastructure that lets AI agents access our data safely, and agentic tooling that answers questions grounded in our semantic layer rather than guessing
• You'll be one of its heaviest users and one of the people who makes it trustworthy: curating the semantic context, catching the answers that look right and aren't, and pushing definitions back into dbt and LookML where they belong. We're not hiring someone to do the same volume of work faster, we're hiring someone who supports multiple teams well using this collection of new tools as a force multiplier
• Partner regularly with Marketing, Sales, Finance and Product teams. Learn their goals, their metrics, and the decisions they're actually stuck on
• Own analysis end to end: clarify the real question, build or pick the right dataset, deliver the answer, and make sure a decision follows
• Go deep on our subscription domain, then write it down. Metric definitions, caveats, always-filters, known gotchas. Domain knowledge that only lives in your head doesn't scale, and scaling it is the point of this role
• Build analytics assets people trust without asking you first: models in dbt, explores in LookML, dashboards that hold up
• Use our agent tooling as a force multiplier and contribute back to it. Feed it semantic context, flag wrong answers, harden the definitions it depends on
• Contribute to the data platform where it unblocks you. Small model and pipeline improvements, debugging discrepancies, helping out when something breaks
• Translate in both directions: business context into robust analysis, data reality into language a non-technical stakeholder can act on