From click‑ops and custom apps to code
Managing the dbt at scale isn't about creating projects; it's about building a data foundation all teams can trust.
Our organization supports ~3,000 dbt platform projects across 6 accounts and two VPCs: one for DEV, one for PROD. Until recently, deployments depended on a custom-built promotion system we'd cobbled together to work around restricted UI access in PROD. Every new feature dbt Labs shipped meant we had to rebuild internal components before our teams could use it.
This session walks through how we replaced that entire system with a full Terraform implementation built on the official dbt platform provider. We'll cover our design decisions, module patterns, drift-elimination strategies, and the onboarding model that lets teams own their infrastructure through GitOps with consistency, auditability, and self-service built in.
Check out more sessions
- Breakout session
From 14-hour batches and poor documentation to AI-ready data: SafetyCulture's dbt rebuild
Yuna(Yunnan) Tang / SafetyCultureThiago Baldim / SafetyCultureView session - Breakout session
Shrink the surface: governed self-service for analyst-owned dbt pipelines
Jinze Xin / LiftoffView session - Peer exchange
Beyond the bottleneck: position your Analytics Engineering team as a strategic force
Kasey Mazza / HubSpotView session
