The Stacked Data Podcast

Jose Garcia didn't just grow Skyscanner's data organisation from 13 to around 60 people — he made sure impact grew with it, across the data science, marketing and ad tech teams he leads. We unpack the challenges of prioritising the right problems instead of the most interesting ones, building leadership layers that don't bottleneck through him, and why he thinks great data science has never really been about the models. We also get into how AI tools like Claude are beginning to change the skills, hiring decisions and day-to-day ways of working inside modern data teams. We cover: * How Skyscanner's data org grew from 13 to 60 without losing focus * Why business impact is harder to prove than most data teams expect * Jose's method for prioritising the right problems, not the interesting ones * What actually changes about leadership once a team crosses a certain size * How AI is reshaping who data teams hire and what they're hired for A practical conversation about data science, leadership, scaling teams and the impact of AI.

Monzo went from zero rules to one of the most opinionated data architectures in fintech — and cut warehouse costs by 40%+ doing it. Bruno Campos, Analytics Engineer at Monzo, joins Harry to unpack the rebuild of Monzo's entire data platform — tens of thousands of dbt models, hundreds of teams, one BigQuery warehouse, and a migration that's still only 30-40% done. Bruno breaks down Monzo's new "OOM" architecture: four fixed layers, data contracts between teams, and an internally-built tool (ModelGen) that turns a YAML file into standardised SQL. The result — faster builds, easier onboarding, and serious cost savings that show up on the GCP bill. They also get into why "data" as a discipline is still, in Bruno's words, "incredibly immature" — and what it'll take to fix that. In this episode: * Why Monzo moved from a free-form warehouse to a heavily opinionated one * The four-layer OOM model: landing, normalized, logical, presentation * Interface models — Monzo's version of data contracts between teams * How ModelGen automates standardisation across 100+ teams * The real cost and time savings from re-architecting at scale * Bruno's take on AI's role in analytics engineering — and why the job isn't going anywhere

Most data orgs scale their AE team as they grow. Wise didn't. Moritz Kerstan runs a lean team of 17 supporting over 300 data practitioners — and he's got a clear thesis on why that's the right call. In this episode, we dig into how Wise structures their analytics engineering function, how the team operates without hierarchical authority, what good prioritisation actually looks like, and the honest truth about building with LLMs inside a large regulated fintech.

Ed Mancey has a take that a lot of data leaders won't like — and he's got two years of results to back it up. As the data team lead at Synthesia, Ed has built his function around a single idea: the data team's job is to be a multiplier for the business, not a bottleneck. That means owning platforms and tools, not seats in strategy meetings. It means drawing hard lines on responsibility. And it means trusting your business users to actually use what you've built. In this episode, Ed breaks down how he's applied that philosophy at one of the UK's fastest-growing AI companies — from a two-year Omni implementation to enabling a sales manager to hit 150% of quota using tools his team built, without a single BI dashboard in sight. We get into: * Why being the C-suite's personal analyst is a career ceiling, not a launchpad * What a truly self-service data function looks like in practice * How Ed thinks about drawing the line between what the data team owns and what it doesn't * The no-blame culture that makes delegation actually work * What AI tools like Cursor and Claude are doing to the day-to-day reality of data work — and what that means for the teams building around them If you lead a data team, or you're trying to figure out where to focus your energy to have more impact, this one's for you.

Most AI projects in financial services stay in a pilot. Moneybox actually shipped one. In this episode, we sit down with Marko Katavic, Director of AI and Decision Intelligence at Moneybox, to get the real story behind Aurora — the in-house AI guidance engine Moneybox built to help their 1.5 million customers better understand and act on their finances. We go deep on what it actually takes to build an AI product inside a regulated environment: the architectural decisions, the trade-offs they made, the things that didn't work, and what the team is focused on next. If you work in data, AI, or fintech — or you're trying to ship an AI product in a high-stakes environment — this is the episode for you. We cover: * What Aurora is, what problem it solves, and why Moneybox built it in-house * The technical architecture behind a production AI product in a regulated context * How they approached FCA compliance, safety, and human oversight * The trade-offs they made — what they prioritised and what they deferred * What good looks like when you're measuring an AI financial assistant * What's next as the product and team continue to evolve