Data Governance · Enterprise AI · Automation · Regulated Financial Services
I design practical, auditable data-governance and AI-enabled solutions for regulated organisations.
My background combines more than 12 years in European financial services with hands-on work in data governance, Python, SQL, APIs, LLM applications, data validation and workflow automation. I focus on translating governance principles and business requirements into controlled, operational solutions.
A DAMA-aligned data-governance operating model for a fictional regulated Swiss insurer.
The project translates governance policy into an operational framework covering:
- five governed data domains;
- Data Owner, Data Steward and Data Custodian accountability;
- a 45-term business glossary;
- data-quality rules and thresholds;
- governance-issue and access-exception management;
- AI use-case readiness, risk classification and human oversight;
- six governance standards and four operational workflows;
- automated structural, schema and referential controls;
- an eight-module Streamlit governance portal;
- continuous validation through GitHub Actions.
View the live governance portal
Explore the repository
- SAP Business AI Workflow — Invoice extraction, supplier and purchase-order validation, approval controls and an auditable decision trail.
- ESG Reporting AI Assistant — Grounded AI support for ESG reporting, regulatory analysis and evidence-based disclosure drafting.
- Python Control Lab — Applied data validation, exception handling and business-process automation.
Data Governance · Data Quality · AI Governance · Governance Controls · Python · SQL · Streamlit · APIs · pytest · GitHub Actions · LLMs · RAG · SAP Business AI · ESG Data
Applying my regulated-industry experience to data governance, AI governance and controlled enterprise automation roles where business accountability, technology and auditability must work together.