Data Analyst

• 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.

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.

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.

From a skills perspective, you bring:

• 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.

Nice to have, and genuinely not required:

• Python, dbt, Looker or LookML, Snowflake or ClickHouse

• Subscription or fintech domain experience

• High-volume data

• 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

• A fully remote environment designed around autonomy and flexibility

• 4-5 weeks of flexible time off annually

• Paid desk at a co-working space

• Workspace budget and continuous learning stipend

• How we think about hiring and team design: Building a Winning Team

• The values that guide how we work: Our Values

• How we work as a remote team: How We Work Remotely at RevenueCat

• What to expect from our interview process: Interview Expectations

• What to expect from comp and employment: Employment FAQ