BigQuery
Google Cloud's serverless, highly-scalable data warehouse that uses SQL and supports petabyte-scale analytics with built-in machine learning capabilities via BigQuery ML.
How RevOps teams use it
RevOps teams at Google Cloud-native organizations use BigQuery to warehouse event streams, marketing touchpoints, and CRM exports for large-scale pipeline and attribution analysis without managing infrastructure.
Example use case
e.g., A RevOps team uses BigQuery to run a multi-touch attribution model across millions of marketing events to calculate true CAC by channel and allocate budget accordingly.
Where it's used
Technical Disciplines
Analytical Engineering
Transforms raw revenue data into clean, trusted models that power reporting, forecasting, and decision-making across the GTM organisation.
BI Engineer
Builds the dashboards, reports, and data models that give revenue leaders a clear, real-time view of pipeline health, performance, and trends.
ML Engineer
Builds machine learning models that power revenue predictions — from churn scoring and lead ranking to pipeline forecasting and pricing optimisation.
RevOps Data Engineer
Designs and maintains the data infrastructure — pipelines, warehouses, and integrations — that ensures revenue data is accurate, available, and actionable.
Industry Verticals
E-commerce
Transaction-based retail across digital channels. Revenue tied to conversion rates, AOV, cart abandonment, and multi-channel attribution.
FinTech
Revenue from interchange fees, lending spreads, AUM, or SaaS licensing. Compliance-aware pipelines and complex fee structure reporting.
Marketplace & Two-Sided Platforms
Marketplace companies operate dual-sided revenue models — take rates on transactions plus optional SaaS subscriptions for sellers — requiring RevOps to instrument GMV, seller acquisition, buyer activation, and cohort-based retention with attribution models standard CRMs cannot support.