Head of Analytics

AirOps•San Francisco, CA

About The Position

As Head of Analytics, you'll own the full analytics function at AirOps — GTM and revenue analytics, product analytics, and company-wide BI. You'll set the philosophy, build the infrastructure, and translate data into the decisions that shape how we grow. This is a hands-on role: you'll be in the data warehouse writing queries, building attribution models, and presenting to the exec team — often in the same day. You'll be building the function from the ground up.

Requirements

6+ years in analytics at B2B SaaS companies, with hands-on experience across GTM, revenue, and product analytics — including building attribution infrastructure and product instrumentation from scratch.
SQL mastery and direct experience in Snowflake, BigQuery, or Redshift; you pull from raw data without depending on other teams.
Experience designing or significantly improving MTA and/or MMM frameworks, with the ability to tie methodology directly to pipeline and revenue outcomes.
Hands-on BI development in Looker, HockeyStack, Tableau, or similar tools; proficiency across the modern GTM stack (HubSpot, GA4, Segment, Clay, HockeyStack).
Ownership mentality — you define success metrics, drive cross-functional projects to completion, and translate complex data into executive-ready narratives.

Nice To Haves

Python or R for advanced modeling (cohort analysis, LTV, incrementality testing)
Experience with dbt or similar data transformation tools
Prior experience building an analytics function at a high-growth startup

Responsibilities

Build and own the end-to-end reporting layer from lead to pipeline to cash, including executive dashboards surfacing pipeline velocity, conversion rates, CAC payback, and full-funnel attribution.
Design and continuously improve a multi-touch attribution (MTA) framework and evaluate marketing mix modeling (MMM) approaches to drive accurate channel ROI and budget decisions.
Serve as the analytics thought partner for leadership — translating GTM performance into clear recommendations on resource allocation and channel mix.
Own product analytics end-to-end: define how we measure engagement, adoption, activation, retention, and expansion across the customer journey.
Connect product usage signals to revenue outcomes — surfacing leading indicators of expansion and churn risk that inform both product roadmap and GTM strategy.
Own the analytics philosophy across the business — setting standards for how data is modeled, consumed, and acted upon across GTM, product, and operations.
Lead the evolution of our BI infrastructure (Metabase → Looker), work directly in Snowflake / BigQuery / Redshift, and partner with GTM, and Data Engineering to ensure clean instrumentation throughout the stack.