Policy-to-Code Governance Engine

Designed and implemented a modular governance automation tool that translates natural-language data policies into executable logic. The system allows non-technical users to write compliance rules like “minors cannot be clients,” which are then parsed by an LLM into readable conditions (e.g., age >= 18) with table-level context.

Once approved, policies flow through a structured pipeline:

Validation: Ensures schema compatibility and prevents workflow disruption.

Clearance Gatekeeping: High-impact actions (like account deletion) require elevated approval.

Code Generation: Converts policies into SQL or Python scripts.

Audit-Logged Deployment: Code is version-controlled and deployed via Liquibase with full audit traceability.

I led the full lifecycle—from architecture and prototyping to scripting, rule modeling, and impact analysis. The system bridged compliance needs with real-world execution, helping teams implement governance without bottlenecks or bureaucracy.

The architecture was selected for presentation at a local PyData conference, highlighting its balance of LLM innovation, rule-based validation, and human-centered governance design.

Tools used: Python, OpenAI API, SQL, Liquibase, Git Skills: Data governance, automation, prompt engineering, policy modeling, architecture design