Analytics Engineering: Internal Risk vs. External Rigor
In this episode, Jack Doherty (VP Analytics at Fresha) joins us to unpack what really changes when analytics moves from an internal function to a customer facing product.
We cover:
Why internal analytics can tolerate speed and risk
Why product analytics demand software level rigor
How testing, release discipline, and ownership change at scale
Why the semantic layer is becoming critical, especially with AI and chat interfaces
What analytics engineers can learn from product engineering teams
This is a practical conversation for anyone building analytics that other people rely on to make real decisions.
Stop Coding, Start Diagramming: How to Build Data Platforms That Deliver If you’re rushing to hire a data engineer before you have a clear business question, you’re doing it backwards.
I’m joined by Teddy Bernays (Freelance Data Engineer) to unpack his “business first” approach. Teddy shares his journey and explains why simplicity and a solid plan always beat the latest tech stack.
His top advice: “Find the problem you want to solve first. Is data the answer? Only then should you start building.”
In this episode, we cover: Why you should hire a Data Analyst before a Data Engineer The “Diagram First” rule for technical projects How to escape the painful world of legacy spreadsheets Finding freelance clients in the real world (get off LinkedIn!) Using AI to finally solve your documentation problems
Watch the full episode on YouTube, or listen on Spotify and Apple Podcasts.
If your company’s first move is buying another tool, this episode’s for you.
I’m joined by Dylan Anderson (Head of Data Strategy, Perfusion) to unpack why so many data strategies fail before they begin and how to build one that actually delivers.
Dylan’s worked with some of the world’s biggest organisations, helping them turn data chaos into clarity. His advice is refreshingly simple:
“Buying more tools doesn’t give you a strategy.”
In this episode, we talk about: ▶️ Why data strategy starts with people, not platforms ▶️ How honest technology builds trust ▶️ Why simplifying your stack beats automating chaos ▶️ The real future of AI and it’s not chatbots
It’s a straight-talking conversation about cutting noise, making smarter technology choices, and building data foundations that actually deliver.
Watch the full episode on YouTube, or listen on Spotify and Apple Podcasts.
Three Things to Kill Before You Build Another Dashboard – how to rebuild trust by cutting sprawl, service-desk habits, and KPI overload
Most data teams hope the next dashboard will deliver clarity, but every new report often adds to the noise. In this episode of Data Matas, Aaron speaks with Phil Thirlwell (ex-Worldpay/FIS) about the three things every data leader should kill before building another dashboard: dashboard sprawl, reactive “service desk” culture, and KPI overload.
From inheriting 600 Power BI dashboards to showing how co-developing metrics builds ownership, Phil explains why the best data teams act more like product teams: focused, deliberate, and accountable. They also look ahead to why AI and automation won’t fix messy foundations, and how the future of BI is conversational, if your data is clean enough to answer the questions.
What you’ll learn
✅ Why dashboard sprawl erodes clarity and confidence ✅ How to escape the “service desk” trap and prioritise outcomes over outputs ✅ Why trimming KPIs forces better decisions and stronger alignment ✅ How co-developing metrics with the people who generate data builds trust ✅ Why AI/automation can’t rescue messy reporting foundations ✅ Why the future of BI is conversational, not static dashboards
Watch the full episode on YouTube, or listen on Spotify and Apple Podcasts.
Most of the modern data stack has transformed, pipelines, compute, governance. But BI? It’s still the same dashboards and reports we were using 20 years ago. Expensive, read-only, and delivering the worst ROI in the stack.
In this episode of Data Matas, Ollie Hughes, CEO ofCount, joins Aaron Phethean to share why BI tools eroded trust, why AI won’t fix reporting chaos, and how data teams can escape the “service trap” to become real decision-making partners.
You’ll learn:
▶️ Why BI tools are the worst ROI in the modern stack ▶️ How the “service trap” caps your team’s value ▶️ Why AI makes reporting faster but not better ▶️ How to build trust in data beyond accuracy alone ▶️ Why ruthless prioritisation is the ultimate lever for data leaders
This is a practical, candid conversation about the real challenges data teams face — and how to refocus BI on clarity, trust, and decisions that matter.
Watch the full episode on YouTube, or listen on Spotify and Apple Podcasts.
Hypebeast withheld access for 10 weeks, teased value, and turned demand into near-universal adoption.
In this episode of Data Matas, Aaron speaks with Sami Rahman, Director of Data & AI at Hypebeast, about what it really takes to embed AI inside a modern business.
Sami shares how his psychology background shapes his approach to adoption, why fear of AI is more about broken safety nets than the technology itself, and why Hypebeast uses AI as a force multiplier — not a replacement for creative teams.
He explains how he deliberately teased AI’s potential for 10 weeks before giving access, using curiosity and scarcity to spark demand. The result? 97% adoption across the company.
Listeners will also hear how Hypebeast prioritises boring but valuable use cases — automating system updates, consolidating research, scanning trends — and why Sami treats AI agents as disposable tools with clear lifecycles, not permanent fixtures.
It’s a grounded, practical conversation about the human side of AI adoption and the discipline it takes to keep hype from overrunning reality.
Watch the full episode on YouTube, or listen on Spotify and Apple Podcasts.
Rebuilding Trust in Your Data Stack – Why Monzo Hit Pause Before Scaling
What happens when you inherit a data platform that’s grown too fast? For John Napoleon-Kuofie, Analytics Engineer at Monzo, the answer was clear: stop, simplify, and rebuild with purpose.
In this episode, John shares how his team is tackling legacy bloat, test fatigue, and the pressure to scale, while keeping trust and clarity at the heart of every decision.
👉 How do you deliver reliable insights when your models number in the thousands and no one knows where they came from?
What you’ll learn:
✅ How Monzo is redefining core concepts like “payment” to simplify their DBT estate
✅ Why default test patterns create more noise than value, and what to do instead
✅ The risk of scaling fast without understanding what you’ve inherited
✅ Why clean joins and clear abstractions matter more than AI
✅ How a culture of bottom-up innovation helps Monzo stay agile
This episode is essential listening for data engineers, analytics leads, and platform owners who want to build systems that last, without sacrificing clarity, accountability, or team sanity.
Watch the full episode on YouTube, or listen on Spotify and Apple Podcasts.
How do analytics engineers grow from writing SQL to designing entire data systems? And why do most companies still confuse tool mastery with real engineering skill?
In this episode of Data Matas, Oleg Agapov (Senior Analytics Engineer at Hiive) shares what it actually takes to go from junior to senior in data—beyond bootcamps, tools, and job titles. He breaks down the core shifts that matter most: thinking in systems, mastering data modelling, and creating structure that scales.
You’ll hear how Oleg is helping build self-serve analytics inside a fast-moving fintech startup, why most data work fails without discovery, and how AI is changing the role—but not replacing the role—of the analytics engineer.
🎙 Guest: Oleg Agapov, Senior Analytics Engineer at Hiive
Oleg has spent over 15 years in data roles, moving from analyst to engineer to analytics architect. Now at Hiive—a marketplace for private stock—he’s helping design scalable data models and BI tooling that enable business teams to self-serve. Oleg also mentors junior engineers and shares career guidance on LinkedIn weekly, offering a rare combination of technical depth and practical coaching.
⏱ Episode Takeaways & Timestamps
03:40 – Why analysts become engineers (and what tools don’t teach you)
Why Oleg moved from analytics into engineering, and how messy data triggered a career pivot.
08:15 – What junior vs senior actually looks like in analytics engineering
From DBT basics to architecture thinking—how your role shifts as you grow.
12:30 – Data modelling isn’t a feature, it’s a discipline
Why writing queries isn’t enough—and why most engineers only realise this at scale.
17:45 – Building analytics in a three-sided marketplace startup
How Oleg is helping Hiive build self-serve data for a unique financial model.
24:00 – How AI fits into the modern data workflow (and where it fails)
Why LLMs are better reviewers than creators—and why trust still starts with humans.
28:40 – The hidden risk of AI assistants in BI tools
What happened when an AI assistant hallucinated a metric—and nearly caused a decision error.
Who Should Listen?
If you’re an analytics engineer, data modeller, or anyone growing a data team inside a startup or scale-up, this episode will help you move beyond dashboards and into strategic, scalable thinking. Especially valuable for those navigating the shift from IC to senior roles.
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Most data teams aren’t drowning in complexity—they’re drowning in pointless work. In this episode, Nik Walker shares how Co-op is scaling its data infrastructure without sacrificing team wellbeing or burning money on unnecessary pipelines. What makes this conversation stand out is its honesty. It’s a no-fluff, real-world view of leadership, neurodiversity, and doing meaningful work in one of the UK’s largest legacy organisations.
Nik Walker is Head of Data Engineering at Co-op, overseeing the transformation of data systems across a complex, multi-sector enterprise with more than 55,000 employees. He leads with empathy, clarity, and a refreshingly human approach to delivery. Diagnosed with ADHD as a child, Nik brings an unfiltered perspective on what it means to lead neurodiverse teams at scale—without turning the job into performance theatre.
Nik shares a personal story about being told he’d never amount to anything because of his ADHD—and how he now leads one of the UK’s largest data teams. Aaron and Nik discuss the “dashboard hamster wheel” and why many teams spend their time delivering reports that no one reads. The pair explore why LLMs and AI outputs can’t be trusted without strong fundamentals—and what happens when CFOs spot inflated costs from over-engineered workflows.
✔️Slow down to speed up
Build space for proper discovery before building pipelines—batch jobs might be all you need.
→ Prevents over-engineering and protects team time.
✔️Not all data needs real-time syncing
Challenge the default. Nik recommends “right-time” pipelines instead of always-on syncs.
→ Reduces cloud costs and frees up engineering capacity.
✔️Lead neurodiverse teams with structure, not slogans
Safe, consistent conversations beat performative “care culture.”
→ Improves communication and productivity in diverse teams.
✔️Don’t chase AI if the trust isn’t there
AI adoption fails when leaders can’t believe the output. Build confidence in the data first.
→ Ensures reliable insights and stakeholder buy-in.
As AI adoption accelerates and data leaders face mounting cost pressures, this episode delivers timely guidance for navigating modern infrastructure challenges—without over-engineering or overworking the team. Nik’s philosophy aligns with a growing shift towards sustainable scaling, especially in regulated or legacy-heavy environments.
This episode is essential for Heads of Data, CTOs, and Engineering Leads who are scaling infrastructure but starting to question the cost—both technical and human. If you’re stuck in reactive delivery cycles or fighting dashboard fatigue, Nik’s approach will give you a smarter way forward.
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💬 What’s one lesson from David’s approach you’d apply to your own team? Drop your thoughts in the comments.
🎧 Available on YouTube, Spotify, Apple Podcasts, and all major platforms.
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Why you should hire a Data Analyst before a Data Engineer
