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AI & Data Engineering Transformation Executive Helping Enterprises Become AI-Agent-Driven Competitive Powerhouses | AI Ethics & Governance | Ex-Amazon & Google
The next competitive divide will not be between companies that modernize their operating models and those that don’t. It will be between organizations that fundamentally redesign how they operate around data, intelligent systems, and real-time, autonomous decision-making—and those that layer AI onto legacy structures. My career has centered on helping enterprise leaders navigate exactly that transformation. I’m a globally recognized technology executive with more than 20 years of experience building and scaling AI, data platforms and high-performance computing systems inside complex, regulated enterprises. I’ve led large-scale initiatives spanning 400+ cross-functional stakeholders and $500B to $1B+ budgets, including the architecture of trading, risk, and data platforms for GSIBs, major banks, and leading hedge funds. After years of leading enterprise transformation initiatives, I made an intentional move into deeply hands-on roles at AWS and Google to work directly on cloud infrastructure, large-scale distributed systems, advanced analytics, and next-generation, cloud-native data & AI architectures. I went from teaching an AI model to say "good morning" to helping architect enterprise-critical platforms that improve how institutions assess risk, allocate capital, accelerate decision-making, and create transparency under intense regulatory scrutiny. That experience prepared me for my next chapter: helping midmarket and enterprise companies transition from traditional operating models to modern, data-fluent organizations that prioritize the ethical use of AI to change how we do business. My expertise spans advanced analytics at scale, cloud-native and HPC platforms, [Quantum Computing], regulatory and quantitative risk systems, enterprise governance and infrastructure strategy, and open-source and distributed computing architectures. I’m a
The University of Chicago Booth School of Business
Master of Business Administration (MBA), Quantitative Finance & Strategic Management
January 1, 2004 – January 1, 2007
Western Illinois University
Master's Degree, Computer Science
January 1, 1999 – Present
Western Illinois University
Bachelor's Degree, Quantitative Information Science
January 1, 1985 – Present
Amazon Web Services (AWS)
Principal Architect - Strategic Accounts
March 1, 2022 – May 1, 2026
New York, United States
Principal Architect - Capital Markets
January 1, 2021 – March 1, 2022
New York, United States
M&T Bank
Senior VP, Enterprise Data & Analytics
June 1, 2018 – July 1, 2020
Buffalo, NY
Options Clearing Corporation
VP, Data Management & Analytics
June 1, 2015 – August 1, 2018
Bank of America - Merrill Lynch
VP – Enterprise Architect / Manager
June 1, 2010 – June 1, 2015
Greater Chicago Area
The Bradford Group
Sr. Director of Enterprise Architecture
June 1, 2008 – June 1, 2010
Greater Chicago Area
GE Healthcare
Lead Global Enterprise Architect
June 1, 2003 – June 1, 2009
Greater Chicago Area
Project Management Professional (PMP)
Project Management Institute
June 24, 2026 – Present
Google Cloud Certified Professional Cloud Security Engineer
June 24, 2026 – Present
AWS Certified Professional Solutions Architect
Amazon Web Services (AWS)
June 24, 2026 – Present
Google Cloud Certified Professional Cloud Network Engineer
June 24, 2026 – Present
Google Cloud Certified Professional Cloud Architect
June 24, 2026 – Present
Oracle Certified DBA (OCP)
Oracle
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's extensive experience across diverse industries (finance, retail, healthcare) and major tech companies (AWS, Google) indicates adaptability and a broad perspective. Their emphasis on challenging assumptions and fostering innovation (e.g., with interns building a real-time analytics system) suggests a cultural alignment with environments that value continuous improvement and new ideas. However, the target role of 'ML Engineer' typically implies a more hands-on, deep technical specialization in ML algorithms, model development, and deployment, which is not explicitly detailed in their experience. While they have led AI transformation, direct ML engineering experience is not evident, which might be a cultural fit gap for a pure ML engineering role.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership, strategic thinking, and the ability to drive complex organizational change, emphasizing the importance of people, trust, and clarity in transformation initiatives. Their experience in highly regulated environments suggests a disciplined and structured approach to problem-solving and project execution. The focus on business outcomes over pure technology indicates a strong operational fit for roles requiring strategic impact.