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Data Analytics & AI Engineer
Combining Data Analytics, Enterprise Risk Management & AI Engineering to automate reporting, answer complex business questions, and transform risk data into executive-ready insights.
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In this blog we explore why designing a process to manage your reporting matters, how it impacts your organization, and what you can do to solve it.
Read More →Eliminated manual data collection errors and reduced processing time by 85% by engineering automated pipelines that validate 23K+ monthly snapshots across hundreds of thousands of records using custom Python scripts, Oracle SQL, and Polars—preventing production incidents and maintaining regulatory compliance.
Accelerated executive risk reporting by 92% (from 1-2 hours to ~10 minutes) by building interconnected ERM reporting system that analyzes relationships across processes, risks, controls, and issues using async parallel extraction and DuckDB aggregations—enabling real-time decision support for 50+ stakeholders.
Compressed development timelines from weeks to hours by architecting AI-powered ETL pipelines and modular frameworks using Claude API and custom Python automation—delivering production-ready analytics solutions that scale across enterprise data volumes while maintaining code quality and testing standards.
Supported Federal Reserve examination readiness and proactive risk mitigation by serving as data intelligence SME—identifying critical patterns across enterprise risk data, synthesizing insights for executive reporting, and building analytics frameworks that support ongoing monitoring and regulatory compliance requirements.
Looking for someone who can combine data engineering, AI tooling, and regulatory expertise to solve complex analytics problems at scale.