dbt
An open-source transformation framework that enables analysts and engineers to define, test, and document data models using SQL within a version-controlled workflow.
How RevOps teams use it
RevOps and data teams use dbt to build clean, tested, documented transformation layers in the warehouse — converting raw CRM, marketing, and product data into reliable business metrics that power dashboards and models.
Example use case
e.g., A RevOps team uses dbt to create a staging model that standardises Salesforce opportunity close dates, then builds downstream ARR and pipeline velocity metrics that feed their Looker dashboards.
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.
Data Governance Engineering
Establishes and enforces the data standards, hygiene rules, and stewardship processes that keep revenue data trustworthy — from duplicate management and field standardisation to master data management across the tech stack.
ML Engineer
Builds machine learning models that power revenue predictions — from churn scoring and lead ranking to pipeline forecasting and pricing optimisation.
Platform Engineer
Builds and maintains the core RevOps platform — the foundational layer of tools, APIs, and infrastructure that every other function depends on.
RevOps Data Engineer
Designs and maintains the data infrastructure — pipelines, warehouses, and integrations — that ensures revenue data is accurate, available, and actionable.
RevOps Systems Architect
Designs the overall revenue technology stack — selecting, connecting, and governing the systems that support the entire customer lifecycle.
Industry Verticals
SaaS B2B
Subscription-based software sold to businesses. Long sales cycles, multi-stakeholder procurement, and recurring revenue tracked through ARR, NRR, and churn.
SaaS B2C
Subscription software sold directly to individuals. Self-serve, high volume, short cycles — revenue operations focused on activation, retention, and reducing involuntary churn.
FinTech
Revenue from interchange fees, lending spreads, AUM, or SaaS licensing. Compliance-aware pipelines and complex fee structure reporting.