GTM Analytics Lead

AirOps
New York City, San Francisco or RemotePosted 19 March 2026

Job Description

GTM Analytics Lead ABOUT AIROPS AirOps is the first end-to-end content engineering platform built for the AI era. In a world where discovery is shifting from traditional search to AI-driven platforms, we help brands get found—and stay found. We are currently in a phase of hyper-growth, having 5x’d our revenue in the last year by helping marketing teams at Ramp, Chime, Carta, and Rippling turn content quality into a durable competitive advantage. Our platform equips marketers to navigate the new discovery landscape, prioritize high-impact opportunities, and create accurate, on-brand content that earns citations from AI and trust from humans. Backed by Greylock, Unusual Ventures, Wing VC, and Founder Collective, we are building the intelligent systems that will empower the next generation of marketing leaders. AirOps is headquartered in San Francisco, New York and Montevideo. ABOUT THE ROLE We're hiring a GTM Analytics Lead to own the full analytics layer across Marketing, Sales, and Customer Success. This is a build-from-scratch role at a company growing fast. You'll be the first dedicated GTM analytics hire, which means you'll set the infrastructure, define the metrics that matter, and make sure every growth decision is grounded in clean data. This is a hands-on role. You'll write the queries, build the dashboards, design the attribution models, and sit in exec reviews, often all in the same week. WHAT YOU'LL OWN Marketing Analytics - Own full-funnel attribution from first touch to closed won. Build executive dashboards surfacing CAC payback, channel ROI, and conversion rates at every stage. - Design and continuously improve a multi-touch attribution framework. Evaluate MMM approaches to drive budget allocation decisions across paid, content, and SEO channels. - Partner with Marketing to understand what's actually driving pipeline. We invest heavily in content and AEO (Answer Engine Optimization) as growth channels. You'll help us measure what's working. Sales Analytics - Own pipeline reporting: velocity, stage conversion, rep performance, and forecast accuracy. - Surface the signals that matter. Where deals stall, what moves close rate, and how to allocate effort across segments. - Be the analytics thought partner for Sales leadership on quota setting, territory planning, and pipeline reviews. Customer Success Analytics - Build retention and expansion reporting: churn risk signals, NRR trends, health scores, and upsell indicators by segment. - Connect product usage data to CS outcomes, giving the team early warning on at-risk accounts and visibility into expansion triggers. - Partner with CS leadership to define success metrics and measure program effectiveness. What We're Looking For - 5+ years in analytics at B2B SaaS companies, with direct experience across Marketing, Sales, and Customer Success analytics. - SQL mastery and hands-on experience in Snowflake, BigQuery, or Redshift. You pull from raw data without depending on other teams. - Experience designing or materially improving multi-touch attribution frameworks, with the ability to tie methodology to pipeline and revenue outcomes. - Hands-on BI experience in Looker, HockeyStack, Tableau, or similar. Fluency across our GTM stack: HubSpot, GA4, Segment, Clay, HockeyStack. - Ownership mentality. You define success metrics, drive cross-functional projects to completion, and present complex data as clear narratives for exec audiences. Preferred - Python or R for advanced modeling (cohort analysis, LTV, incrementality testing) - Experience with dbt or similar transformation tools - Prior experience building an analytics function at a high-growth startup OUR GUIDING PRINCIPLES 1. Extreme Ownership 2. Quality 3. Curiosity and Play 4. Make Our Customers Heroes 5. Respectful Candor BENEFITS - Equity in a fast-growing startup - Competitive benefits package tailored to your location - Flexible time off policy - Parental Leave - A fun-lovin ... (truncated, view full listing at source)
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