Data Analyst, Revenue Operations

Title: Data Analyst, Revenue Operations

Date: February 2026  
Reports to: Manager, Sales Operations 

Location: Remote, United States  

Position Summary 

The Data Analyst, Revenue Operations owns performance analytics and insights across the end-to-end GTM engine, spanning Marketing, SDR, Sales and Account Management. This role is responsible for turning operational data into clear narratives that improve conversion rates,��pipeline quality, win rates, retention, and expansion.  

This role focuses on measurement, insight, and strategic guidance-answering what’s working, what’s not, and why across the customer journey.   

You will partner closely with Sales, Marketing, Customer Success, Finance, and RevOps leadership to connect pre-sale decisions to post-sale outcomes (retention, churn, expansion, revenue quality) helping the business understand which customers we should win, how we should win them, and how those decisions affect long-term revenue. 

Key responsibilities 

 

Own analysis across the full revenue lifecycle: 

• Provide defined GTM-level insights that connect demand generation to pipeline outcomes, including: 

• Pipeline Outbound vs. Inbound 

• Win-rate and cycle-time trends by source/segment to inform targeting 

• Deal quality indicators (e.g., source mix, segment fit, vertical) 

• Support weekly/monthly business review readouts with decision-oriented narratives from business owners (what changed, why it matters, recommended next steps). 

• Answer ad hoc questions as capacity allows through an intake/prioritization process. 

 

Funnel, Pipeline & Velocity Insights 

• Own the funnel model and reporting cadence across key stages (e.g., Inquiry/MQL → Meetings → Qualified → Pipeline → Closed-Won). 

• Deliver recurring reporting and insights by: 

• Channel, campaign/program, vertical/segment, tiering, and source type 

• Conversion rates, velocity, and drop-off analysis between stages 

• Build and maintain standard Marketing performance dashboards, including: 

• Funnel and waterfall views 

• Leading indicators (volume, speed-to-lead, meeting rates, quality metrics) 

• Pipeline contribution (sourced and influenced, where definitions exist) 

• Produce actionable insights that translate into clear recommendations: 

• Where to invest (segments/channels/programs) 

• Where leakage occurs and what behaviors correlate to better outcomes 

• Quality and efficiency trends (not just volume) 

 

GTM Handoff & SLA Measurement (Funnel Integrity Reporting) 

• Own measurement and recurring reporting for GTM handoffs and SLAs across the revenue funnel 

• Build standardized handoff da