About the role The Data Analyst partners with business teams across Marketing, Sales, Finance, People, Operations, and Product to turn business questions into actionable analysis. Working closely with the existing analytics owner, you will develop expertise in subscription metrics, build reliable datasets and dashboards, and help improve AI-assisted analytics through accurate definitions and verified outputs. Your responsibilities Work directly with business teams to understand their goals, metrics, and decisions. Own analyses from clarifying the question and selecting or building datasets through delivering findings and supporting decisions. Develop and document subscription domain knowledge, including metric definitions, required filters, limitations, and known data issues. Build and maintain dbt models, LookML explores, and dashboards that business teams can use independently. Use AI agents in analytical workflows, provide semantic context, identify incorrect answers, and improve the underlying definitions. Contribute targeted improvements to data models and pipelines, investigate discrepancies, and help resolve data platform issues. Translate business needs into reliable analysis and explain findings clearly to non-technical stakeholders. Experience and skills 3+ years in data analytics, business intelligence, business analysis, analytics engineering, or a similar role. Experience serving as the direct analytics partner to a business team such as Marketing, Sales, or Finance. Strong SQL skills and the ability to work independently in a data warehouse. Experience owning datasets and dashboards used by non-technical teams. Comfort working with Git, branches, pull requests, and code reviews in version-controlled analytics repositories. Daily experience using AI agents and verifying their outputs. Clear written communication, particularly when explaining analytical limitations and caveats. Curiosity about business domains, a willingness to investigate unexpected results, and the ability to structure work with incomplete requirements. Useful additional experience Python, dbt, Looker, LookML, Snowflake, or ClickHouse. Subscription businesses or fintech. Working with high-volume data.

Benefits

Equity options with a 10-year window to exercise vested options. 4-5 weeks of flexible time off annually. A paid desk at a co-working space. Workspace budget. Continuous learning stipend. A remote working environment built around autonomy and flexibility.