数据分析师
Data Analyst
RevenueCat 为应用业务提供基础设施和工具，帮助他们构建、运营并优化其变现模式。自 YC S18 批次毕业以来，我们已成长为移动领域的默认变现平台。
我们已进入 50% 以上的新开通订阅应用，年交易额超过 160 亿美元，并帮助从独立开发者到 OpenAI 移动团队的各类用户理解并增长他们的收入。
我们是一个以远程办公为主的团队，成员超过 150 人，分布在 25 多个国家。我们践行着真正的价值观：客户至上、持续交付、主动负责和保持平衡。
这并不适合所有人。你在这里构建的系统管理着数十亿美元的资金，影响数亿终端用户，我们对此非常重视。你将与一些最聪明、最有动力的人一起努力工作，对所交付的内容保持高标准。如果这听起来令人兴奋而非疲惫，继续阅读吧。
#### 职位描述
我们正在招聘一名数据分析师，与运行 RevenueCat 业务的团队紧密合作，包括市场、销售、财务、人力资源、运营、产品等。
目前我们分析团队最有价值的东西不是 SQL，而是领域知识。知道试用开始实际上意味着什么，为什么跟踪收入和实际收入不同，商店退款如何体现在我们的数据中，以及哪个模型能第一次就正确回答问题。这种知识能够将一个模糊的 Slack 问题转化为一小时内可以采取行动的数据。
因此，你大部分时间会与业务团队一起工作：了解他们想要做出的决策，将模糊的问题转化为分析，并交付他们依赖的数据集和仪表板。你将快速建立这种领域知识，每天与目前负责分析的人员合作。他将成为你最亲密的合作伙伴，也是帮助你成长为该领域专家的人。
这个职位令人兴奋的第二点是我们期望你如何工作。我们正在构建一种基础设施，让 AI 代理安全地访问我们的数据，并构建基于我们语义层而不是猜测来回答问题的智能工具。你将成为其中最频繁的使用者之一，也是让其值得信赖的人：整理语义上下文，发现看起来正确但不正确的答案，并将定义重新推回到 dbt 和 LookML 中。我们不是在寻找一个更快完成相同工作量的人，而是在寻找一个支持多任务处理的人。
查看英文原文
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](https://www.revenuecat.com/blog/company/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](https://app.notion.com/p/Building-a-Winning-Team-69e431c4c3df4b13961523cd93c0e9bf?pvs=21)
- The values that guide how we work: [Our Values](https://www.revenuecat.com/blog/values/)
- How we work as a remote team: [How We Work Remotely at RevenueCat](https://www.revenuecat.com/blog/how-we-work-remotely-at-revenuecat/)
- What to expect from our interview process: [Interview Expectations](https://app.notion.com/p/RevenueCat-Interview-Expectations-1bdcb1a108b0800c8941def0f076f9e2?pvs=21)
- What to expect from comp and employment: [Employment FAQ](https://app.notion.com/p/RevenueCat-Compensation-Employment-Overview-184cb1a108b0801398d4e525928597ab?pvs=21)