David Akokodaripon
Engineering Manager
Newark, New Jersey, United States
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David Akokodaripon is an Engineering Manager and enterprise data platform leader with over a decade of experience building and scaling mission-critical data and AI infrastructure across global industries. Currently leading software engineering for DISPATCH, the world's largest open-pit mining fleet management system at Komatsu, David oversees engineers across FMS application, data, integration, and analytics platforms serving global mining operations where platform downtime directly impacts production at scale.
Prior to Komatsu, David served as Senior Data Engineering and Architecture Leader at Kyndryl, where he directed enterprise-scale data platform modernisation for major organisations across steel manufacturing, pharmaceuticals, and retail, delivering cloud-native architectures that generated measurable revenue uplift and significant reductions in annual platform costs.
David's career spans four countries and diverse industries, including mining, financial services, pharmaceuticals, hospitality, and retail giving him a rare cross-industry perspective on what it takes to build data platforms that reliably support enterprise AI at scale. He holds an MBA in Strategic People Management, a degree in Computer Science, and a degree in Electrical Information Engineering, with published peer-reviewed research in distributed financial risk systems, LLM reliability, and scalable big data analytics.
A Professional Member of the British Computer Society, Fellow of the National Institute of Professional Engineers and Scientists, and Member of the Canadian Association of IT Professionals, David brings both the technical depth and executive leadership perspective to challenge how organisations think about data infrastructure and what it truly takes to make enterprise AI production-ready.
Area of Expertise
Topics
Future-Proofing Data & AI Governance: Open Architectures, Policy-as-Code, and Adaptive Compliance
As companies speed up their use of AI and make data more available, the old ways of managing things—made for fixed separate systems—can't handle the changing risks complex rules, and needs of unorganized data. My session will present to data leaders practical new ways to manage things built on open data storage systems automated policies written as code, and AI-checkable compliance steps. Attendees will gain from my session real examples of technical plans and useful ways to bring together management for organized and unorganized data and AI models across different cloud setups. They will Find out how to make management ready for the future with standards that work together built-in security ideas, and flexible controls that grow with new ideas.
My session will focus on four main points:
1)Framework: A modular governance model integrating data, AI, and cloud security.
2)Case Study: Example of how to Implement an open lakehouse governance stack (Delta Lake + MLflow + Open Policy Agent).
3)Tools: Production code snippets used for policy automation, drift detection, and metadata lineage in a real-life business use case.
4)Action Plan: Prioritized steps on how they can modernize governance without stifling innovation in their respective organizations.
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