Senior Data Analyst, GTM Analytics

Role overview

Recharge is hiring a Senior Data Analyst, GTM Analytics to support the Go-To-Market organization (Sales, Business Development, Revenue Operations, and Demand Generation). Reporting into the central Data & Analytics team, you’ll partner with GTM leadership to improve performance measurement, uncover growth opportunities, and scale self-serve analytics.

This role blends:

• Analytics Engineering: build trusted, well-modeled datasets in dbt and reporting in Looker.
• Traditional Analytics: apply business acumen and quantitative skills to deliver actionable insights.
• Data Product Management (AI-first): use LLMs/AI to design and ship data products that improve time-to-insight and self-serve adoption.

Responsibilities

• Build and maintain durable, documented, tested dbt models powering GTM reporting (pipeline, funnel, conversion, bookings, forecasting, rep performance, account health).
• Create dimensional models (facts/dimensions), define metrics, and deliver “one version of the truth” datasets.
• Develop and maintain Looker explores, dashboards, and standardized reporting for self-serve use.
• Implement data quality checks, monitoring, and model documentation to ensure stakeholders can confidently act.
• Partner with data engineering to improve upstream instrumentation, source hygiene, and pipeline reliability.
• Collaborate with GTM stakeholders to translate business questions into analysis plans and decision frameworks.
• Use SQL (and Python as needed) to analyze trends, diagnose performance, and recommend actions (e.g., funnel bottlenecks, win-rate drivers, deal velocity, territory/segment opportunities).
• Communicate GTM narratives that connect data to decisions for leaders and cross-functional partners.
• Proactively monitor performance and adoption signals to surface risks, anomalies, and opportunities.
• Identify high-leverage “insight bottlenecks” and design data products (e.g., KPI hubs, automated pipeline risk alerts, self-serve funnels, guided explorations).
• Leverage LLMs/AI to accelerate insight workflows (metric explainers, natural-language Q&A over curated datasets, automated summaries/anomaly narratives, guided templates).
• Define success metrics for data products (e.g., reduced time-to-insight, increased self-serve usage, fewer ad-hoc requests, improved forecast accuracy).
• Operate independently in ambiguous environments; frame problems, generate hypotheses, and drive outcomes.
• Manage multiple priorities and communicate via Slack, docs, and stakeholder readouts.
• Contribute to team standards (modeling patterns, documentation, code review, stakeholder management).

Requirements

• Strong quantitative analytics skills with the ability to translate business questions into clear analysis plans.
• Proven ability to build and maintain analytical data models and reporting.
• Hands-on experience with SQL; Python experience preferred/used as needed.

Nice-to-haves

• Experience building analytics engineering workflows with dbt and scalable reporting in Looker.
• Experience designing AI/LLM-assisted data products to improve self-serve adoption and time-to-insight.