Data Analyst

Summary

RevenueCat is a leading monetization platform for mobile applications, processing over $12B in annual purchase volume. They are seeking a Data Analyst to work closely with various business teams, providing insights and analysis to facilitate decision-making and enhance their understanding of subscription metrics.

Responsibilities

• 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

Skills

• 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
• 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
• Curiosity is the thing we're actually screening for: 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
• Python, dbt, Looker or LookML, Snowflake or ClickHouse
• Subscription or fintech domain experience
• High-volume data

Qualifications

Must Haves

• 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
• 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
• Curiosity is the thing we're actually screening for: 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

Nice to Haves

• Python, dbt, Looker or LookML, Snowflake or ClickHouse
• Subscription or fintech domain experience
• High-volume data

Benefits

• Competitive equity in a fast-growing, Series C startup backed by top-tier investors, including Y Combinator
• 10-year window to exercise vested equity options
• Fully remote and flexible work environment
• 4-5 weeks of suggested time off annually for mental, physical, and emotional recharge
• $2,000 USD for workspace setup
• $1,000 USD annual stipend for continuous learning