data-warehouse-experimentation

# Data Warehouse Experimentation A senior data scientist's playbook for running experiments natively out of BigQuery, Snowflake, or any modern data warehouse, with metric definitions in dbt and statistical analysis in SQL or Python. Most companies that run experiments at scale use a dedicated platform. Statsig, Optimizely, LaunchDarkly with experimentation, PostHog, Amplitude Experiment. The platforms are good. They handle assignment, instrumentation, and analysis in one product, and the SQL-savvy data team does not have to reinvent the variance reduction wheel. There is a different operational model that mature data teams increasingly choose: warehouse-native experimentation. Assignment happens in code or via feature flags. Exposure events fire to the warehouse like any other event. Metrics are defined as dbt models. Statistical analysis runs as SQL or in a Python notebook against warehouse data. The "experiment platform" is just your existing data stack. This skill covers when warehouse-native is the right call, the architecture, and the specific techniques that make it work: assignment patterns, exposure logging discipline, metric definitions in dbt, t-tests and CUPED in SQL, sequential testing, and the pitfalls that take down homegrown setups. When to use this skill: deciding between platform vs warehouse-native, building a warehouse-native experiment infrastructure, auditing an existing one, or running a specific experiment when the platform of record cannot handle ...