S4E2 - Will AI Replace Data Engineers? Dashboards, Semantic Layers & What Dies Next

S4E2 - Will AI Replace Data Engineers? Dashboards, Semantic Layers & What Dies Next

"AI won't replace data engineers. But engineers using AI will." In Season 4, Episode 2, we sit down with Julian [add last name + role] to unpack what's actually changing in data engineering — and what's about to disappear. We get into: → Why dashboards as we know them are dying (and what replaces them) → Does AI really need a semantic layer? Julian's answer might surprise you → The low-code trap quietly racking up tech debt across data teams → Where AI is genuinely useful today: tech debt, testing, and data governance → The one skill that still matters most when AI can write the code → A spicy closing question for the next guest about cloud cost ⏱ Chapters 00:00 — Intro 01:30 — Julian's path into data 04:00 — The career pivot that changed everything 07:30 — How his team is adopting AI (and who resists) 10:00 — Does AI need a semantic layer? 13:00 — Local models are closer than you think 15:30 — Where AI is actually working: tech debt, tests, governance 19:00 — What the data industry is getting completely wrong 23:00 — The belief Julian held 3 years ago that's now wrong 26:00 — A question for the next guest

S4E1 - Why AI Breaks Without a Semantic Layer.

Season four, episode one of the Data Matas Podcast. Aaron Phethean sits down with Kevin Sampson, the first data hire at Vertex Service Partners, who joins us after four and a half years at Amazon. The conversation is about what it actually takes to build a data and analytics platform from zero. We cover dashboard sprawl at Amazon, the gap between insights and answers, why "confidently wrong" is the worst failure mode an LLM can have inside a real business, and a hot take on whether AI even needs a semantic layer anymore. This is the first episode of our new format. Every guest faces a hot take. One question they have never heard before, designed to challenge how they think on the spot.

S3E7 - Building High-Performance Data Teams Starts With People, Not Tools

Data Engineering as a Team Sport: Coaching, Accountability, and Pragmatism In this episode, Sam Wrench, Lead at Reality Mine, joins us to explore what actually makes data teams perform at a high level. We cover: Why data engineering breaks down when teams optimise for tools instead of people  How coaching principles translate directly from elite sport to data leadership  Why dogfooding your own data creates faster feedback loops and better quality  How to think about AI as a managed colleague, not an autopilot  Why pragmatic, security-first tooling often beats chasing industry hype This is a grounded conversation for anyone building data platforms that real teams and real decisions depend on.

S3E6 - Analytics Engineering: Internal Risk vs. External Rigor

Analytics Engineering: Internal Risk vs. External Rigor | Ft. Jack Doherty (Fresha) The stakes are higher than ever for Analytics Engineers. When your data becomes a core, customer-facing product, the game changes. Jack Doherty (Head of AE, Fresha) discusses the massive difference between internal and external analytics: Risk vs. Rigor: Why internal projects can move fast (risk), but product-facing data demands DevOps-level rigor, testing, and governance. Real-Time Data: The technical shift from scheduled batches to CDC for meeting customer demands for speed and consistency. The Missing Link: Why the Semantic Layer is the future of AE, crucial for codifying business logic and powering accurate AI/Chat interfaces. A must-watch for any AE treating data as a product.

S3E5 - Building Data Platforms That Actually Solve Business Problems

Stop Coding, Start Diagramming: How to Build Data Platforms That Deliver If you're rushing to hire a data engineer before you have a clear business question, you’re doing it backwards. I'm joined by Teddy Bernays (Freelance Data Engineer) to unpack his "business first" approach. Teddy shares his journey and explains why simplicity and a solid plan always beat the latest tech stack. His top advice: "Find the problem you want to solve first. Is data the answer? Only then should you start building." In this episode, we cover:  ▶️ Why you should hire a Data Analyst before a Data Engineer  ▶️ The "Diagram First" rule for technical projects  ▶️ How to escape the painful world of legacy spreadsheets  ▶️ Finding freelance clients in the real world (get off LinkedIn!)  ▶️ Using AI to finally solve your documentation problems