Senior Data Analyst, GTM Analytics - CANADA (Remote)

Our GTM Analytics team owns the metrics that run the business for Sales, Marketing, and Client Success. We're past the phase of standing up basic reporting — we're now building a modern, agentic analytics practice, including Lighthouse, our internal BI-as-code platform, where dashboards, metrics, and semantic definitions live in version-controlled code rather than scattered across disconnected tools.

This role is a chance to shape that next chapter. You'll partner directly with Sales, Marketing, and CS leaders to answer their hardest questions about pipeline, funnel conversion, campaign performance, and retention, while helping build the infrastructure and AI-forward workflows that let the whole GTM org get trustworthy answers faster.

You'll report to our Senior Manager of Analytics and work in tight, two-week sprints alongside Analytics Engineering, so you'll see your work ship and get used — fast.

• 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.

• 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.

• 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.

• Push our analytics workflows toward agentic AI: using AI agents to accelerate analysis, automate recurring reporting, and cut down on manual, one-off dashboard requests.

• Bring a point of view on where AI can replace manual analyst work versus where it can't — you should be as comfortable prompting and evaluating an agent as writing the SQL yourself.

• Operate in two-week sprints, planning and prioritizing your own backlog against team OKRs alongside your manager.

• Give clear, proactive status updates to stakeholders rather than waiting to be asked.

• 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.

• Strong communicator who can frame a recommendation clearly for a non-technical stakeholder and defend it under questioning.

• Comfortable with the accountability of owning a deliverable through to closure, not just the analysis in the middle.

• Energized by ambiguity and fast iteration — two-week sprints mean you'll ship early and often.