Why AI Agents Are Replacing Traditional Data Engineering w/ Matt Glickman

Are AI agents replacing traditional data engineering tasks? I sat down with Matt Glickman, CEO & Co-founder of Genesis Computing and former Snowflake and Goldman Sachs executive, for a conversation about where data engineering is actually heading. Most organizations today are stuck in "demo mode" or using LLMs as expensive code completers, simply writing code faster to feed legacy data architectures. But if you look at the industry from first principles, humans were never meant to be data machinery. In this episode, Matt and I break down how true agentic data systems work, why current enterprise architectures will fail under agent-scale query volumes, and what it really takes to capture institutional knowledge before it leaves the building. We also cover Matt’s front-row seat at Goldman Sachs during the 2008 financial crisis, his early days turning Snowflake into an enterprise giant, and how AI-native companies will outpace traditional enterprises. If you're building, architecting, or leading teams in the AI and data space, this is a conversation you don't want to miss. Timestamps 00:00 – Capturing knowledge and Matt Glickman’s background 03:00 – Lessons from Goldman Sachs and surviving the 2008 financial crisis 07:30 – The "Strat" persona: Combining tech, business, and data in one brain 10:25 – Joining Snowflake in 2014 & solving the "big user" concurrency problem 16:30 – The spark behind Genesis Computing and agentic data workflows 19:40 – Why companies get stuck in the AI demo phase and generate "code cruft" 22:00 – Building an enterprise context graph: Digital twins for data architecture 28:00 – First principles: What does a true agentic data engineer look like? 33:50 – Rethinking data architectures for AI agents, not humans 39:45 – The Tesla FSD analogy: How enterprises should transition to AI automation 42:10 – The biggest opportunity in AI: Capturing distributed human knowledge 48:10 – Running an AI-native company from day one