The Challenge
A healthcare media and events company came to Wallabi asking for an n8n email automation expert. After the first build, the CEO said it wasn't what she needed. After a second pivot to data access, she said that wasn't it either. What surfaced on the third attempt: the company had no coherent data architecture. Data was dirty, scattered across systems, duplicative — and before any automation could work, they couldn't even answer a basic question about who was in their database.
What They Built
Wallabi built a complete data infrastructure using Snowflake, dbt, Fivetran, and the Wallabi platform — with Claude Code authoring nearly all code and documentation — delivering a medallion architecture with 140 data models, Tableau reporting, and reverse ETL into CRMs in six weeks.
Wallabi's engagement began as an n8n email automation request. After two failed pivots, the real problem emerged: the client had no coherent data architecture. Data was dirty, duplicated, and scattered across disconnected systems. Wallabi scrapped the original scope and redesigned the engagement entirely. They built a modern data stack from scratch — Snowflake as the warehouse, dbt for transformation, Fivetran for ingestion, and the Wallabi platform as the application layer. A CI/CD process was built around Claude Code, which wrote approximately 99% of the code and documentation throughout. Prior session context was stacked directly into the repository so every team member benefited from accumulated learnings. This enabled Wallabi to roll out nearly 140 independent data models in six weeks — work that would have taken six to nine months with a traditional team. On top of the warehouse, they added Tableau for reporting and Snowflake Intelligence for natural language queries. Reverse ETL pipelines pushed clean data back to the client's CRMs. The final outcome: Wallabi's work became a direct input to the client's board narrative and 2026 growth strategy.
AI Role
Built in 6 weeks on a production-ready medallion architecture — work that would have taken 6 to 9 months with a traditional team
Infrastructure
• Snowflake (cloud data warehouse) • dbt (data transformation and modeling) • Fivetran (data ingestion/ELT) • Wallabi platform (application layer) • Tableau (reporting and dashboards) • Snowflake Intelligence (natural language query layer) • Claude Code (AI-assisted development — ~99% of code)
Integration Points
• Source systems → Fivetran → Snowflake (raw/bronze layer) • Snowflake → dbt transformation → medallion architecture (silver/gold layers) • Gold layer → Tableau dashboards for reporting • Gold layer → Snowflake Intelligence for NL querying • Gold layer → reverse ETL → client CRM and marketing automation tools
Mid-market companies in services, media, or events that know they need to do something with AI but keep hitting dead ends — especially those who have already bought the right tools and still aren't seeing results from them.
