How a Media Firm Cut 8 Months of Data Work to 8 Weeks
A healthcare media firm's data was scattered and duplicative. Claude Code wrote 99% of a new Snowflake stack — shipping 140 data models in eight weeks instead of eight months.
8 weeks
Deployed in 8 weeks, not 8 months
The Challenge
A healthcare media and events company came to Wallabi requesting a simple email automation workflow. Through iterative discovery, the real problem emerged: data was dirty, scattered across disconnected systems, and highly duplicative. The company had no unified concept of a "person" or "company" in their data architecture. Without a coherent data foundation, any workflow built on top would be unreliable — and their audience engagement and event recruitment strategy had no data bedrock to operate from.
What They Built
Wallabi scrapped the original scope and rebuilt the client's entire data architecture from the ground up — Snowflake as the warehouse, dbt for transformation, Fivetran for ingestion, and the Wallabi platform for the application layer. They implemented proper dimensional modeling and a medallion architecture. Claude Code wrote approximately 99% of the code and documentation, enabling the team to roll out close to 140 independent data models in six weeks — work that would normally take six to nine months. On top of the warehouse, they built roughly 10 intelligent data applications. Reverse ETL pushes clean, harmonized data back into the client's CRM and marketing automation tools for activation.
Wallabi's original mandate was a simple email automation workflow. Through discovery, the real problem surfaced: the client's data was dirty, scattered across disconnected systems, and highly duplicative, with no unified concept of a person or company in the architecture. Wallabi scrapped the original scope and rebuilt from the ground up. The warehouse foundation was Snowflake, with Fivetran handling data ingestion and dbt managing transformation. Proper dimensional modeling and a medallion architecture were implemented to create a clean, reliable data 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, enabling cumulative learnings to benefit all team members across sessions. Nearly 140 independent data models were rolled out in six weeks — a project that would normally take six to nine months in traditional development. On top of the warehouse, Wallabi built roughly ten intelligent data applications. Snowflake Cortex and Snowflake Intelligence added natural language query capabilities. Reverse ETL pipelines pushed clean, harmonized data back into the client's CRM and marketing automation tools for activation.
Infrastructure
Snowflake (cloud data warehouse) • dbt (transformation and dimensional modeling) • Fivetran (data ingestion/ELT) • Wallabi platform (application layer) • Snowflake Intelligence (natural language query layer) • Snowflake Cortex (AI/ML features within Snowflake) • Tableau (dashboards and reporting) • Claude Code (AI-assisted development — ~99% of code)
Integration Points
Source systems → Fivetran ingestion → Snowflake raw layer • Raw layer → dbt transformation → medallion architecture (bronze/silver/gold) • Gold layer → Tableau reporting + Snowflake Intelligence NL queries + Snowflake Cortex ML • Gold layer → reverse ETL → CRM and marketing automation tools for activation
Impact
A data infrastructure project that would typically take six to nine months in traditional development was completed in eight weeks, with approximately 140 data models rolled out — all powered by Claude Code writing nearly all code and documentation.
Weeks-Long Wait for Reports Eliminated
Business teams that previously waited weeks for a single data report now have real-time access. The client gained an instantaneous view of their data and operational metrics — a capability they had never had before.
Data Foundation Reshapes 2026 Board Growth Strategy
The project directly contributed to the company's board narrative for 2026 growth, enabling them to target new audiences and recalibrate their entire brand strategy. The CEO had not anticipated needing this level of strategic visibility — it emerged as a consequence of having clean, structured data.
Implementation Complexity
Best Fit For
Growth-oriented CEOs, COOs, and CIOs at companies between $75M–$750M in revenue that are feeling competitive pressure — growth has plateaued, they're losing deals to AI-native competitors, or clients are threatening to leave. Industries: services & consulting, media & events, supply chain & logistics.
