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Data

Data Analyst

RevenueCat · EMEA, LATAM, Canada, United States · full-time · Remote

Datafull-timeRemoteEMEA, LATAM, Canada, United StatesUSD 167,000 – 167,000 / yearly
Data Science & AnalyticsAny
RemoteJobs editorial overview

Strategic Overview

Data Analyst at RevenueCat is a data opportunity centered on the specialist responsibilities and outcomes described in the employer's listing.

The published role emphasizes work such as 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…

The role is listed for EMEA, LATAM, Canada, United States with remote working arrangement, giving candidates the practical context needed to assess fit before applying. Relevant signals in the listing include Data Science & Analytics, Any.

Job seeker snapshot

Key information at a glance

Role focusData
LocationEMEA, LATAM, Canada, United States
WorkplaceRemote
Employmentfull-time
Experience3+ years
SalaryUSD 167,000 – 167,000 / yearly
PostedOct 13, 2026
Last seenOct 18, 2026
Role context

About This Role

RevenueCat gives app businesses the infrastructure and tools to build, run, and improve their monetization. Since graduating from YC's S18 batch, we've grown into the default monetization platform for mobile.

We're in 50%+ of newly shipped subscription apps, we process $16B+ in annual purchase volume, and we help everyone from a solo developer to the OpenAI mobile team understand and grow their revenue.

We're a remote-first team of 150+ people across 25+ countries, guided by values we actually practice: Customer Obsession, Always Be Shipping, Own It, and Balance.

What the role delivers

Key Responsibilities & Objectives

  • 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.
Candidate blueprint

Required Qualifications & Candidate Blueprint

  • This isn't the right fit for everyone. The systems you’ll build here manage billions of dollars and touch hundreds of millions of end users, and we don’t take that lightly. You'll be expected to work hard and hold a high bar for what you ship, alongside some of the sharpest, most driven people you've worked with. If…
Compensation & benefits

Salary & Benefits

Published compensation: USD 167,000 – 167,000 / yearly

  • 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
Employer-provided source content

Full Job Description

The following is the complete job description supplied by the source. We sanitize unsafe markup and remove external links, but do not shorten or rewrite the employer's source content.

RevenueCat gives app businesses the infrastructure and tools to build, run, and improve their monetization. Since graduating from YC's S18 batch, we've grown into the default monetization platform for mobile.

We're in 50%+ of newly shipped subscription apps, we process $16B+ in annual purchase volume, and we help everyone from a solo developer to the OpenAI mobile team understand and grow their revenue.

We're a remote-first team of 150+ people across 25+ countries, guided by values we actually practice: Customer Obsession, Always Be Shipping, Own It, and Balance.

This isn't the right fit for everyone. The systems you’ll build here manage billions of dollars and touch hundreds of millions of end users, and we don’t take that lightly. You'll be expected to work hard and hold a high bar for what you ship, alongside some of the sharpest, most driven people you've worked with. If that sounds energizing rather than exhausting, keep reading.

The role

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.

What you will do

  • 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.

About you

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

What we offer:

  • 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

Interviewing at RevenueCat

Our interview process is rigorous on purpose. We want to be sure that everyone we bring on is genuinely excited about this work, aligned with how we operate, and ready to meet our high bar for performance. Curious what that actually looks like? Read more in our blog post on how we hire at RevenueCat, including tips to help you succeed.

More on how we work:

  • 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

RemoteJobs original analysis

Editor’s Market Perspective (Our Custom Analysis)

Why This Position Matters

This data opening is a useful opportunity for candidates whose experience matches Data Science & Analytics, Any. Its strongest career signal is the scope of the work itself — particularly Partner regularly with Marketing, Sales, Finance and Product teams. Learn their goals, their metrics, and the decisions they're actually stuck on. — rather than a generic title alone.

Resume & Application Strategy

For the application, mirror the employer's language where it truthfully matches your background: foreground Data Science & Analytics, Any, quantify outcomes from comparable work, and place the most relevant experience near the top of the resume. If the listing specifies tools, platforms, leadership scope, or domain experience, use those exact terms in context instead of presenting an unrelated keyword list.

Source transparencyjobicy

We organize source-provided information into this independent job-seeker overview. Compensation is shown only when the source provides it; the official listing remains the authority for current requirements and application instructions.