Does your data pipeline deserve to exist? How EF Education built a cost and value layer with dbt and Cortex Agent

Most analytics engineering teams can tell you if a pipeline is running. Very few can tell you if it's worth running.

At EF Tours, we built mart_metadata: a dbt-native observability layer that tracks build consumption, Snowflake credit costs, Power BI usage, and downstream query patterns, stitched into a single model of what each data product actually costs and who uses it. The goal was simple: give every team a factual answer to "what is this data product worth?"

We'll walk through the scoring methodology, the cost attribution layer in dbt, the semantic layer in Snowflake, and how we used a Cortex Agent to make that intelligence conversational, so anyone can get a grounded answer without writing a query.

You'll leave with a concrete scoring formula for dbt model performance, a pattern for attributing Snowflake credits to individual data products, and a working example of a Cortex Agent as the interface layer.