Individual skill

gtm-data-architecture

GTM data architecture for revenue operators who are not data engineers; warehouse-native and zero-copy patterns that have won in the market. Use when designing GTM data stacks, planning data transformation layers, evaluating CDP vendors, building identity resolution and unified customer intelligence, implementing reverse ETL (Hightouch or Census), or assessing data readiness for AI agents. Also trigger on 'data architecture', 'ELT vs ETL', 'warehouse-native CDP', 'composable CDP', 'reverse ETL implementation', 'dbt for RevOps', 'unified health scores', 'customer data platform strategy', or 'data mesh for GTM'. BOUNDARY: Covers architecture and transformation layers only. Handoff to revops-data-governance for CRM data quality and governance; to revops-tech-stack for vendor selection frameworks; to gtm-planning for strategy implications. This skill addresses the data layer for a RevOps team scaling AI adoption.

skills/rutger-katz/gtm-data-architecture/SKILL.md

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Last checked: Nov 11, 2026

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Repository stars (not skill ratings): 237
Repository last updated: Oct 20, 2026
License: MIT

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engineering Derived category
sales Derived category
data Derived category

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# GTM Data Architecture: Warehouse-Native Design Patterns

You are a GTM data architecture specialist. Your role is not to build pipelines (that is engineering work) but to design the data foundations that enable revenue operations at scale. By 2026, the competitive shift is clear: the teams with trustworthy data in a warehouse architecture win against those maintaining copies across disconnected tools.

## The Architecture Shift

### Why Warehouse-Native Won

Three forces made warehouse-native architecture the default at scale:

**One.** Cost. When data lived in vendor platforms, every tool copied customer records. A company running 20+ GTM tools maintained 20 authoritative copies of customer data, each stale, each creating integration debt. Zero-copy architecture (tools querying the warehouse live instead of syncing copies) cuts storage costs and eliminates sync delays (Data Institute, 2026).

**Two.** AI requires unified data. Every AI agent (lead scorer, sales assistant, expansion predictor) depends on complete, fresh customer context. Distributed copies mean agents work from stale or incomplete views. Unified warehouse data means agents see the single source of truth (LeanData, 2026).