To transform event-level data from Snowplow in Snowflake using dbt:

• Setup: Install dbt and configure connection to your Snowflake instance containing Snowplow event data
• Raw Data Models: Create dbt models that reference Snowplow's enriched event tables, typically structured as atomic events with rich context
• Data Cleaning: Build dbt models to clean data, filter relevant events, flatten JSON contexts, and standardize event properties
• Enrichment & Aggregation: Use dbt to join Snowplow events with customer profiles, product catalogs, and other business data, creating sessionized and user-level behavioral metrics
• Dimensional Modeling: Create fact and dimension tables optimized for analytics, including user journey analysis, conversion funnels, and behavioral cohorts

This approach enables scalable, maintainable transformation of Snowplow's rich behavioral data for analytics and machine learning applications.