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

Stuart Fenton holds arguably the most unique job in UK data and AI — Head of AI at Reading FC, and the first person to hold that title in English football. His path here wasn't exactly conventional. Telecoms. Applied research at the University of Surrey's 5G Innovation Centre. Government AI advisory. And then — a completely faceless tweet to the club's owner. In this episode, we get into: • Why most AI vendors in sport are selling buzzwords, not results • How to build genuine AI capability on a League One/Two budget • The blueprint Stuart is creating that smaller clubs could actually replicate • Computer vision, recruitment AI, and injury prevention — what's real, what's hype • Why commercial acumen is harder to teach than any technical skill • How to build trust and culture in an environment that's wary of change This isn't a conversation about futurism. It's about doing things properly — with limited resources, in a traditional industry — and making it actually work.

In this episode of the Stacked Data Podcast, we explore how modern data teams can drive real business impact by combining commercial thinking with a product mindset. I'm joined by Ryan, a data leader at Viasat, who has built the company's only commercially-driven data roadmap across six data teams. With experience spanning analytics engineering, data product management, and team leadership, Ryan brings a practical perspective on how to bridge the gap between technical excellence and business value. We discuss what it really means to build a commercially-driven roadmap, why many data teams struggle to connect their work to outcomes, and the mindset shift required to become a truly commercial data professional. We also dive into the concept of "data as a product" — what it looks like in practice, how to prioritise effectively, and how to measure success through adoption, satisfaction, and ROI. Ryan shares a real example of consolidating 200 dashboards into 40, and what that taught him about product thinking, stakeholder alignment, and delivering meaningful impact. If you're looking to move beyond reactive reporting and towards proactive, product-led data work, this episode is packed with practical insights.

In this episode of the Stacked Data Podcast, Harry sits down with Adam Sorka  from Hyper Cubed to tackle one of the biggest gaps in the industry right now: Why so many AI projects never make it past experimentation — and what it actually takes to deliver real value. Adam has built a reputation as a pragmatic (and often sceptical) voice in the AI space. In this conversation, he breaks down what's really driving the current wave of AI adoption — and why much of it is still fuelled by hype, not outcomes. They explore how to properly identify and validate high-value AI use cases before writing a single line of code, what "AI readiness" actually means beyond buzzwords, and how to think about testing, governance, and risk in production systems. A big theme throughout is the role of humans in the loop — why removing them too early creates more problems than it solves, and how the best teams design AI systems that augment, rather than replace, decision-making. Finally, Adam shares how to measure real impact and what it takes to scale beyond a single successful use case — turning AI from a side experiment into a meaningful business capability. If you're a data leader or practitioner trying to cut through the noise and build AI that actually delivers, this episode is packed with practical frameworks and hard-earned lessons.

The Data Science Identity Crisis | Anurag Gangal (Spotify) on Data Roles, Analytics Engineering & AI What does a data scientist actually do anymore? In this episode of the Stacked Data Podcast, Harry sits down with Anurag Gangal from Spotify to unpack one of the biggest challenges in modern data: the growing confusion around data role titles. From data scientists and analytics engineers to product analysts, machine learning engineers, and more, the data landscape has become increasingly hard to navigate. Anurag shares the story behind his framework for understanding data roles, why he built his now-popular quadrant model, and how it can help both companies and individuals make better decisions. The "Data Scientist" identity crisis - Anurag's Substack [https://selectstar.substack.com/p/data-scientist-identity-crisis] They explore why so many businesses still use the title data scientist to describe completely different jobs, how that creates problems in hiring and team design, and what it means for people trying to build careers in data. The conversation also dives into generalists vs specialists, the evolution of the modern data stack, and how AI could reshape the future of analytics, data science, and self-serve data work. Whether you're a data leader, analytics engineer, data analyst, product analyst, machine learning engineer, or someone trying to break into data, this episode will help you better understand where the industry is heading. In this episode, we cover: * Why the term data scientist has become so confusing * The difference between analytics engineers, data analysts, product analysts, and ML engineers * How to think about specialisation vs generalisation in data teams * The real cost of poorly defined data roles * How Anurag's data role quadrant model helps bring clarity * How to think about your career path in data * How AI may change the future of data science, analytics engineering, and self-serve analytics Guest: Anurag Gangal, Spotify Host: Harry Gollop Podcast: Stacked Data Podcast If you enjoyed this episode, make sure to like, comment, and subscribe for more conversations with the people building the future of data. Our sponsor is Omni, an AI-powered BI platform that helps people use data to do their best work. Whether users prefer AI, Excel, point-and-click exploration, or SQL, Omni enables fast, trusted answers from a governed semantic model. The Stacked Data Podcast is produced by Cognify — a specialist recruitment partner for teams working across the modern data stack, machine learning & AI. If you're looking to hire top data talent or exploring your next move in data, feel free to reach out to the Cognify team — we're always happy to help and chat through the market. #DataScience #Spotify #AnalyticsEngineering #DataAnalytics #MachineLearning #DataCareers #ModernDataStack #AI #DataLeadership #ProductAnalytics #DataEngineer #Analytics #StackedDataPodcast

What does it take to run over 1,500 experiments a year — and still maintain speed, quality, and impact? In this episode, Harry sits down with Dmitry Zolotukhin [https://www.linkedin.com/in/dimazolotukhin/overlay/about-this-profile/?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAAAhItnwB4sPSrzvmZ8E27qMch_Me0dFnbEI], VP of Analytics at Flo, to unpack how one of the world's leading health and wellness apps built a truly data-driven culture of experimentation. They explore Flo's journey from early-stage testing to a mature experimentation framework, the technical infrastructure behind it, and how the team coordinates hundreds of tests at once without losing focus on user experience or business value. You'll learn: * How Flo scaled experimentation from small tests to 1,500+ annual experiments * The tools and frameworks powering experimentation at scale * Strategies for balancing speed vs. quality in testing * How to foster a culture of learning and experimentation across teams * Real examples of how data-driven insights shaped Flo's product roadmap

On this episode of the Stacked Data Podcast, Harry Gollop sits down with Hugo Lu, co-founder of Orchestra, to tackle one of the most common debates in modern data teams: should you build your own tools or buy off-the-shelf solutions? Hugo shares his experiences on both sides of the decision, practical frameworks for evaluating cost, opportunity, and long-term value, and real-world examples of when building or buying was the right call. Whether you're a Head of Data, an engineer, or just curious about tooling strategy, this episode provides actionable insights to help your team make smarter, strategic decisions.

This week, I'm joined by Timothy Chan, Head of Data at Statsig. Tim has a fascinating background — he started his career as a scientist developing life-saving drugs before pivoting into data. He went on to become a Staff Data Scientist at Meta and now leads the data function at Statsig, one of the world's leading experimentation platforms, recently acquired by OpenAI. In this episode, we dive into the power of experimentation and how Meta embedded it into every aspect of product development. We unpack: ⚙️ What great experimentation really looks like 🏗️ How to build a world-class experimentation function 💬 How data teams can use experimentation to influence business decisions ⚖️ The balance between speed, rigour, and impact 👥 Why stakeholder collaboration is the true differentiator of high-performing data teams Tim shares brilliant insights from his time at both Meta and Statsig — including how to think about experimentation as a cultural capability, not just a technical one. If you care about driving real business impact with data, this is a must-listen.