A show to explore all data matters.
From small to big, every company on the market, irrespective of their industry, is a data merchant. How they choose to keep, interrogate and understand their data is now mission critical. 30 years of spaghetti-tech, data tech debt, or rapid growth challenges are the reality in most companies.
Join Aaron Phethean, veteran intrapreneur-come-entrepreneur with hundreds of lived examples of wins and losses in the data space, as he embarques on a journey of discovering what matters most in data nowadays by speaking to other technologists and business leaders who tackle their own data challenges every day.
Learn from their mistakes and be inspired by their stories of how they've made their data make sense and work for them.
This podcast brought to you by Meltano - "Unlock the Insights in your Data"
S4E3 - Trust, Constraints, and AI: The New Rules for Building Data Teams That Actually Work
“The best data teams aren’t built around superheroes. They’re built around trust.”
In Series 4, Episode 3, we sit down with Toby Henley Smith, Senior Analytics Engineering Manager at Moneybox, to talk about what makes data teams genuinely valuable to a business, and why some accepted “best practices” might actually be getting in the way.
We get into:
→ Why great analysts bring trust and rationality to decisions, not just reports
→ Why startups need psychological safety to experiment, get things wrong and learn quickly
→ How over-engineering and “best practice” stacks can become expensive distractions
→ Whether AI agents writing SQL could make heavyweight semantic layers less relevant
→ How AI is shifting engineering from writing code to defining problems and reviewing solutions
→ Why AI could accelerate the development of junior engineers rather than replace them
→ What tighter budgets and deliberate constraints can teach data teams about building better systems
→ Why cutting cloud costs starts with deciding what the business genuinely cannot live without
At the heart of the conversation is a simple question: are we building better data systems, or just more complicated ones?
19 May 2026
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
7 May 2026
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.
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