What if scaling your data platform meant starting from scratch?
In this episode of Data Matas, John Napoleon-Kuofie, Analytics Engineer at Monzo, shares what it really takes to rebuild trust in your models - inside one of the UK’s fastest-growing digital banks.
From inheriting 1,000+ undocumented DBT models to challenging the purpose of legacy tests, John walks through the decisions his team is making to improve data quality, reduce noise, and future-proof their platform. It’s a candid, practical conversation about choosing clarity over complexity, and learning to say no to work that doesn’t deliver value.
👉 In this episode, you’ll learn how to:
Rebuild inherited models by starting with real-world concepts, not assumptions
Reduce alert fatigue by testing only what matters (and ignoring the rest)
Prepare your data architecture before layering on AI or self-serve tools
Design systems that future engineers can actually understand
Foster a culture where bottom-up innovation drives real change
Watch the full episode on YouTube, or listen on Spotify and Apple Podcasts.
👤 John Napoleon-Kuofie on LinkedIn
👤 Aaron Phethean on LinkedIn
🎧 Data Matas Podcast
🌐 Matatika Website