How I Solved for Data Validation with AI

Key Takeaways

• Built AI skill linking GitHub PRs to Snowflake validation notebooks
• Leveraged Claude Code for automated refactoring in dbt projects
• Auto‑generated queries to ensure backward compatibility of data changes
• Applied principle of least privilege to secure Snowflake access
• Reduced validation time, enabling faster, reliable releases during hack weeks

Pulse Analysis

Data validation has long been a bottleneck for analytics engineers, especially when AI tools rewrite transformation code. Traditional manual checks struggle to keep pace with rapid, AI‑generated changes, leaving teams uncertain about downstream data integrity and backward compatibility. The hack‑week experiment highlighted this friction, prompting a search for a solution that could both generate code and verify its impact in real time.

The team’s answer combined Claude Code’s natural‑language programming capabilities with a custom AI skill that orchestrates a GitHub pull request and a Snowflake notebook. When Claude proposes a dbt refactor, the skill injects the change into a PR, then spins up a Snowflake notebook pre‑loaded with queries that compare pre‑ and post‑change data states. Security is baked in through the principle of least privilege, granting the notebook only the roles needed for validation. This automated pipeline eliminates manual query writing, reduces human error, and ensures that any data mutation is immediately scrutinized.

The result is a faster, more reliable release cycle that scales with AI‑augmented development. By automating backward‑compatibility checks, teams can confidently push AI‑generated code, shortening time‑to‑value and lowering the risk of data regressions. The approach also sets a precedent for broader AI‑driven governance frameworks, where code creation and compliance verification coexist seamlessly, empowering analytics engineers to focus on insight generation rather than repetitive validation tasks.