Responsibilities

• GTM Analysis & Decision Support
• Deliver decision-ready analysis across Sales, Marketing, and Client Success: pipeline health, funnel conversion, campaign and channel performance, cohort/retention, and GRR/NRR drivers.
• Partner directly with Sales, Marketing, and CSM stakeholders to scope questions, frame recommendations, and see analysis through to a decision — not just a dashboard handoff.
• Bring structure to ambiguous asks — translate a stakeholder's question into a clear, testable analytical approach.
• Data Modeling & Quality
• Build and maintain dbt models and marts for Sales, Marketing, and CS domains, with Salesforce as a core source system.
• Own data quality and documentation for your domain: clear definitions, source-of-truth tables, and QA that stakeholders can trust without double-checking.
• Lighthouse & Agentic Analytics
• Contribute to Lighthouse, our BI-as-code platform, as we build it out — help define how dashboards, metrics, and semantic models get written, versioned, and reviewed like software.
• Note: Lighthouse is an active build, not a finished platform — you'll be shaping it, not just using it.

Requirements

• 5+ years in analytics/BI, with meaningful time supporting Sales, Marketing, or Customer Success functions at a fast-growing company. Staff-level candidates should bring deeper scope — larger or more ambiguous problems owned end-to-end, and a track record of setting technical direction, not just executing against it.
• Expert SQL and hands-on dbt experience modeling GTM data.
• Comfortable with Python for analysis and automation — this isn't a heavy ML/stats role, but you know how to script your way out of manual work.
• Experience with Salesforce as a data source — you understand CRM objects, fields, and the quirks of modeling data.
• Familiarity with a modern data stack: Fivetran or similar EL tooling, and a cloud warehouse (we run Snowflake; BigQuery experience also welcome).
• Experience with at least one modern BI tool (Sigma, Hex, Tableau, Looker, or similar) is a strong plus, not a hard requirement — what matters more is that you understand how modern BI organizations operate: governed semantic layers, self-serve dashboards, and BI-as-code approaches.
• Genuine curiosity about agentic analytics — you've used AI agents or LLM tools to speed up analysis, and you want to help define what that looks like at scale.
• We build with Claude day-to-day, so hands-on experience with Claude Code (or a similar coding agent like Codex) is a big plus — you'll use it to move faster on dbt models, scripting, and analysis, not just as a novelty.