Data Analyst

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 - 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 and $1,000 USD annual stipend for continuous learning