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

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 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 , 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.

Welcome back to the Stacked Data Podcast — where we explore what it really takes to build impactful data teams in the modern world. This week’s episode is all about stepping out of the ticket queue and into the strategic driver seat . I sat down with Adam Cassar , Director of Analytics at Wise , to explore how analytics teams can break free from the reactive reporting cycle and become genuine business partners. This conversation is packed with real-life examples, practical strategies, and hard-earned lessons from Adam’s experience leading high-performing teams. We cover: ✅Why many analytics teams end up in a service-provider role — and how to shift that perception ✅The biggest barriers to becoming more strategic (and how to overcome them) ✅How to proactively influence business decisions (not just report on them) ✅What skills, mindsets, and relationships actually matter if you want your team to have impact Whether you're an IC or leading a data team, this episode is for anyone who wants to stop being a dashboard factory — and start driving real change in the business. 🔁Share it with someone who’s ready to level up their data career #StackedDataPodcast #AnalyticsEngineering #ModernDataTeam #DataLeadership #Wise #DataStrategy

𝐁𝐞𝐲𝐨𝐧𝐝 𝐭𝐡𝐞 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 – 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐓𝐫𝐮𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐟 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 Analytics Engineering has become one of the most in-demand roles for modern data teams in recent years. The role goes far beyond just building clean data pipelines and data models, but how do we actually measure that impact? In today’s episode of the Stacked Data Podcast, we're joined by Ross Helenius, Director of Analytics Engineering & AI Transformation Engineering at Mimecast, to unpack one of the most important (and overlooked) questions in data: 👉 What does success look like for Analytics Engineering: beyond the technical? 𝚆̲𝚎̲ ̲𝚎̲𝚡̲𝚙̲𝚕̲𝚘̲𝚛̲𝚎̲:̲ ✅ The true role of Analytics Engineering in modern data teams ✅ Why measuring ROI is so hard and how you can do this ✅ How to define and track impact beyond pipelines, models, and dashboards ✅ Practical KPIs and strategies to showcase business value ✅ Pitfalls to avoid when proving the value of your data function Ross brings deep experience from the intersection of data, engineering, and AI, and offers actionable insights for data leaders and practitioners alike. Whether you're leading a data team, building one, or looking to become a better AE this episode is packed with value.

Digestible. Defensible. Actionable. What does it really take to drive impact with data? Too often, analysts are left wondering: “Why didn’t anyone do anything with that insight?” This week on the Stacked Data Podcast , I’m joined by Sam Marks , Director of Analytics & Business Strategy at the Boston Bruins , to explore the gap between finding insights and driving action — and how to close it. Sam shares the Digestible, Defensible, Actionable framework he uses to turn analysis into outcomes, covering: ✅ What makes an insight digestible (and where analysts go wrong) ✅ How to make your work defensible and credible ✅ What separates an interesting insight from an actionable one ✅ How to overcome inaction from stakeholders ✅ Real-world examples of the framework in action This one’s packed with tactical advice for any data professional tired of their work getting stuck in slide decks.

Building High-Value Data Teams and the Future of BI with Oliver Hughes, CEO of Count In this episode of the Stacked Data Podcast, we're joined by Oliver Hughes, CEO of Count — a business intelligence platform reinventing how teams collaborate through a flexible, canvas-style interface. Together, we explore: The key principles that make data teams truly impactful Why operational clarity, effective problem-solving, and reducing time to value are so critical How Count's unique approach is transforming data collaboration beyond traditional dashboards The future of BI and how data teams can stay ahead in a fast-evolving landscape Whether you're leading a data team, building one, or looking for smarter ways to drive insights, this episode is packed with valuable lessons. Tune in and discover a smarter, more collaborative future for data!

In this episode of the Stacked Data Podcast , we're joined by Zach from Advancing Analytics to dive deep into the world of modern data platforms. Zach walks us through his career in data and his current role at one of the UK's leading data consultancies. We explore what a modern data platform really is, why companies are investing in them, and what it takes to build one that’s scalable, reliable, and genuinely useful to the business. From core stages and common pitfalls to ensuring business alignment and future-proofing, Zach shares the lessons he's learned delivering platforms for a wide range of clients. We also zoom out to talk about what it’s like working in data consulting—what skills matter, what a typical day looks like, and what makes someone successful at Advancing Analytics. If you're interested in data architecture, consulting, or just want to understand what "modern" really means when it comes to data platforms—this one’s for you.

In this episode of Stacked Data Podcast , I’m joined by Manou , Head of Data & X at Medik8 , to dive into the unique challenges of leading a small data team —and more importantly, how to overcome them. ✅ How do you prioritize when resources are tight? ✅ How can small data teams drive real business impact ? ✅ What strategies help in building a strong team culture ? Manou shares practical strategies on ensuring data teams stay focused on high-value projects, align with business goals, and maximize their potential—even with limited resources. If you’re leading (or part of) a small data team, this one’s for you.

🚀 𝐖𝐡𝐲 𝐭𝐡𝐞 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐋𝐚𝐲𝐞𝐫 𝐢𝐬 𝐭𝐡𝐞 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐏𝐢𝐞𝐜𝐞 𝐢𝐧 𝐃𝐚𝐭𝐚 & 𝐀𝐈 🎙️ Data teams are investing more than ever in AI and analytics, yet many still struggle to make their data truly accessible, consistent, and reliable. One major reason? The semantic layer. Without a curated Semantic Layer, self-serve AI is a pipedream! In the latest episode of the Stacked Data Podcast, I sit down with Sarah Levy , CEO of Euno , to unpack why the semantic layer is often overlooked—and why it’s so crucial for businesses looking to scale AI and analytics effectively. 𝕎𝕖 𝕕𝕚𝕧𝕖 𝕚𝕟𝕥𝕠: 🔹Why so many companies struggle with data consistency and accessibility 🔹What a semantic layer actually is and why it’s a game-changer 🔹How it bridges the gap between raw data and real business impact 🔹The challenges of adoption & why many data teams still don’t have one 🔹How Euno is tackling this problem head-on If you’re working in data and your organisation is pushing for AI, this is a must-listen.

In the world of app-based products, event tracking is the backbone of understanding user behaviour, engagement, and product success. But with so much data available, how do you decide what to track? How do you avoid over-tracking while ensuring your insights drive real business impact? In this episode of the Stacked Data Podcast, I sit down with Matt R, Head of Data at Paired, to break down the art of event tracking—what to track, what to ignore, and how to ensure your analytics strategy is built for action, not overload. 🔥 𝕎𝕙𝕒𝕥 𝕎𝕖 ℂ𝕠𝕧𝕖𝕣: ✅ How to define and prioritize the “right” events to track ✅ Avoiding common pitfalls like over-tracking & inconsistent data ✅ Best practices for documentation, governance, and making event data actionable ✅ How to communicate the importance of tracking to non-technical teams ✅ Practical advice for setting up event tracking in mobile apps from scratch Whether you’re a data professional, product manager, or startup founder, this episode will give you the framework to master event tracking and turn data into real insights that drive growth.