Senior Data Analyst, GTM Analytics

Overview

We’re hiring a Senior Data Analyst to support our Go-To-Market Organization (Sales, Business Development, Revenue Operations and Demand Generation). You will report into our central Data & Analytics team and partner closely with GTM leadership to improve how we measure performance, identify growth opportunities, and scale self-serve insights across the organization.

This role blends:

• Analytics Engineering: building trusted, well-modeled data sets in dbt and scalable reporting in Looker
• Traditional Analytics: applying business acumen, curiosity, and strong quantitative skills to deliver impactful insights
• Data Product Management (AI-first): leveraging LLMs/AI to design and ship data products that meaningfully improve time-to-insight and self-serve adoption

What you’ll do

• Live by and champion all of our core values (#ownership, #empathy, #day-one, and #humility).

• Build and maintain durable, documented, tested models in dbt that power GTM reporting (e.g., pipeline, funnel, conversion, bookings, forecasting, rep performance, account health).
• Design dimensional models (facts/dimensions), metrics definitions, and “one version of the truth” datasets to make analysis repeatable and trustworthy.
• Develop and maintain Looker explores, dashboards, and standardized reporting that enable self-serve by the GTM organization.
• Implement data quality checks, monitoring, and model documentation so stakeholders can confidently act on results.
• Partner with data engineering on upstream improvements (instrumentation, source system hygiene, pipeline reliability).

• Collaborate with GTM stakeholders to clarify business questions and translate them into analysis plans, metrics, and decision frameworks.
• Perform analysis using SQL (and Python as needed) to identify trends, diagnose performance, and recommend actions (e.g., funnel bottlenecks, win-rate drivers, deal velocity, territory/segment opportunities).
• Build GTM narratives that connect data to decisions—clearly communicating insights with business context to leaders and cross-functional partners.
• Proactively monitor performance and adoption signals to surface risks, anomalies, and opportunities—then drive stakeholders toward resolution.

• Identify high-leverage GTM “insight bottlenecks” and design data products to solve them (e.g., standardized KPI hubs, automated pipeline risk alerts, self-serve funnels, guided explorations, executive views).
• Leverage LLMs/AI to accelerate insight workflows—examples might include:
• AI-assisted “metric explainers” embedded into dashboards
• Natural-language Q&A experiences over curated GTM datasets
• Automated insight summaries / anomaly narratives for weekly GTM reviews<