James Steele

Senior technologist with specialized expertise in building enterprise-scale financial technology platforms, combining proficiency in modern data engineering, cloud architecture, and applied machine learning. Architected and implemented comprehensive data infrastructure solutions featuring relational and non-relational databases, RESTful APIs, and automated ETL pipelines orchestrating multi-source data integration. Led successful migration of mission-critical counterparty analytics platform to AWS cloud infrastructure while maintaining Agile delivery cadence. Developed proprietary quantitative credit risk models with machine learning validation frameworks, producing statistical performance metrics for executive decision-making and regulatory reporting. Advanced AI/ML capabilities demonstrated through development of NLP systems for risk-relevant news classification and generative AI applications automating credit analysis workflows. Strong foundation in software engineering principles, evidenced by architecting reusable code libraries, optimizing analytical queries, and establishing team-wide code quality standards through structured review processes. Comprehensive technical documentation expertise supporting governance, model risk management, and regulatory compliance requirements.

As part of the Enterprise Risk Management Department at Fannie Mae, Mr. Steele is responsible for the data analytics and reporting of all past, present and potential counter-parties doing business with Fannie Mae. He utilizes his expertise in databases, SQL, python, SAS and Tableau to effectively and quickly report on industry trends leveraging large data sets. Additionally, he is a project owner of migrating critical databases to AWS as Fannie Mae explores Amazon Web Services.

As a member of the Disclosure Data Governance team, Mr. Steele analyzes processes and systems to identify potential and existing risks affecting daily disclosures of mortgage backed securities. He uses Python, VBA, and SQL to improve processes and automate daily reporting of both the single family and multi-family disclosure departments. While assisting with production support, he is also the lead architect and developer of a newly issued mega reconciliation tool written in object orientated Python and SQL that is used to verify publicly disclosed data at every issuance. Mr. Steele utilizes his knowledge of programing and problem solving for timely problem resolution and thorough analysis of data.