Data Modelling
The ability to design logical representations of business data — defining entities, relationships, grain, and hierarchies — that accurately represent revenue processes and support reliable analytics.
Day-to-day use
Designing the grain and schema for an ARR movements table, mapping Salesforce opportunity stages to booking events, structuring a subscription event log for MRR calculation, and documenting entity-relationship diagrams for new data domains.
Typical tasks
- ·Building a subscription events model that tracks ARR movements (new
- ·expansion
- ·contraction
- ·churn)
- ·designing a lead funnel model with consistent stage definitions
- ·creating a bookings model that reconciles CRM and billing data
Business outcome
Well-designed data models ensure metrics are calculated consistently, data is non-duplicated, and analytical queries run accurately — giving every team a shared, trustworthy view of revenue.
Where this skill is applied
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.
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.
RevOps Data Engineer
Designs and maintains the data infrastructure — pipelines, warehouses, and integrations — that ensures revenue data is accurate, available, and actionable.