15+ years modernizing data ecosystems inside banking, commercial lending, and regulatory reporting — combining hands-on architecture with executive leadership. Today that means embedding machine learning and generative AI directly into governance: anomaly detection, exception management, lineage mapping, and audit-ready reporting.
Generative AI drafts governance artifacts and speeds up lineage and impact analysis — but SME review and control ownership never leave human hands.
ML-enabled monitoring surfaces pipeline failures and reporting variances upstream, before they turn into downstream breaks or audit findings.
Every transformation program is grounded in hands-on data architecture — canonical models, metadata, lineage — before it becomes an executive initiative.
A few programs that show how AI, architecture, and governance come together in practice.
Leading enterprise data-quality and governance transformation across cloud data platforms, regulatory reporting, metadata, lineage, and audit remediation. Applied Microsoft Copilot and generative AI to draft and standardize governance artifacts and accelerate lineage and impact analysis, while building Python- and ML-based monitoring with Azure Data Factory to catch pipeline anomalies and reporting variances earlier in the lifecycle. Also delivered reusable canonical and semantic models for consistent, governed analytics across domains.
Led enterprise-scale migration and integration for Commercial and Wholesale Lending platforms during post-M&A harmonization, implementing automated data-quality controls and AI-assisted exception handling. Partnered with data owners and stewards to define metadata, taxonomy, and conformed datasets across the commercial banking domain, and directed proof-of-concept evaluations for AtScale and Splice Machine.
Modernized regulatory data-governance operations through workflow automation and standardized controls, strengthening metadata, data quality, and audit-readiness practices aligned with BCBS 239, SR 14, and DAMA principles. Connected business glossary terms and taxonomy to technical metadata, and led client-reporting initiatives across hybrid platforms.
Led CAAR analytics work within the SPG Analytics group, designing controls to keep RMBS market data accurate and current. Automated RMBS workflows and validation checks, and built resilient data pipelines in partnership with business, risk, and compliance stakeholders to define KPIs and acceptance criteria.
Copilot ML models, anomaly detection, AI-assisted data-quality patterns, Azure ML, Claude Cowork, N8N.
Metadata, lineage, business glossary, MDM, DAMA, canonical & semantic modeling, Collibra, Erwin.
Azure, AWS, Databricks, Azure Data Factory, Spark, Hadoop, Talend, Alteryx.
SQL Server, Oracle, Teradata, Snowflake, Power BI, Tableau, SAS, MicroStrategy.
Python, SQL, T-SQL, PL/SQL, JavaScript, Java, GitHub, Jenkins, CI/CD.
BCBS 239, FR Y-14, 2052a, SR 14, AUC/A — banking & commercial lending controls.
Open to conversations on AI-enabled data quality, governance modernization, and enterprise platform strategy.