Podcasts
The Data Hustle Podcast
Research in podcast form: Tony and I talk careers and consultancy with practitioners as we write our upcoming book on data.
AI Won't Fix Your Culture: Power Distance, Vibe Coding & Human Judgment - #15 Jimmy Pang
Your data architecture ends up looking like your company's culture, whether you planned it or not. In this episode of The Data Hustle, we speak with Jimmy Pang, founder of Data Biz and former data and analytics leader at Foodpanda, Delivery Hero, HelloFresh and Vestiaire Collective, about building data teams across Hong Kong, Berlin and Paris, and what eight years of doing that teaches you about culture, AI and data mesh. Jimmy starts with payments and fraud at Foodpanda, where cash on delivery and bot attacks turned a finance topic into a data problem, then walks through how German, Chinese and French working cultures differ: meeting notes, power distance, work-life balance, and why a federal country like Germany produced a data landscape with eleven ways to answer one question while Hong Kong defaulted to a central model. His conclusion is that culture is the first thing to assess before any data or tech change. We then move to the economics: why Jimmy thinks the future of data work is in Asia, why AI is the next bookkeeper-to-finance-team productivity leap rather than the end of the profession, and the difference between an Apple approach and a Google approach to shipping with AI, including the comprehension debt you take on when the model does the thinking for you. On communication, he takes the Culture Map as his guide and keeps everything explicit: one-on-ones, written retrospectives, and a documented way of working that survives a two-city team. Finally, Juan and Jimmy dig into data mesh: why it is a big-company problem, why German companies can agree on the principle and fail on execution, what monolithic repos, Impala and two-hour CI runs did to his day, and why you should take data mesh as a principle, never word for word. Find Jimmy: LinkedIn: https://www.linkedin.com/in/jimmy-pang-hk603/ Data Biz (Substack): https://jimmypang.substack.com/
Surviving the Mean Data Streets: Semantic Layers, Friction & Passion - #14 Chris Tabb
Thirty years in data and the same mistakes keep coming back with new names. In this episode of The Data Hustle, we speak with Chris Tabb, co-founder and Chief Commercial Officer of LEIT DATA and self-appointed mayor of the Mean Data Streets, about what three decades in BI, data and consulting teach you about the industry's habit of reinventing itself. Chris started in the back office at Cognos in the 1990s, went independent as a BI consultant in 1998, and has been hands-on through databases, ETL, BI and big data ever since. He explains why the semantic layer was born with Business Objects universes and Cognos catalogs, why "context layer" is his umbrella for graphs, ontologies, semantic layers and vector databases, and why the one-person modelling bottleneck that killed the old semantic layer is finally being automated away. We get honest about consulting as a lifestyle: why you should not go into it if you want a nine-to-five and your weekends, why passion is a hiring filter Chris can spot in minutes, and why the extra money on a day rate comes at a cost. Chris lays out his definition of business value as an evidenceable positive effect on company objectives, why ROI is a word he hates, and his three kinds of friction: delivery, business and operational. We also cover AI and scale: why rolling Claude out to every developer without structure produces piles of code nobody understands, why TOGAF-style architecture artefacts are suddenly worth maintaining again as the spine for agents, and how Chris ran a whole demo pipeline on a few cents of tokens. Finally, Chris walks through the journey from freelancer to founding LEIT DATA: picking a specialism (early Snowflake), running lean until you can afford the first hire, growing to a hundred people and back, HR and bench forecasting, what he looks for when hiring, and why community only works when you show up without an agenda. Find Chris: LinkedIn: https://www.linkedin.com/in/chris-tabb-datatips/ LEIT DATA: https://www.leit-data.com/
Scaling a Data Consultancy: Pricing, Branding & Clients - #13 David Cohen
Most data consultants are great at the technical work and bad at everything that turns it into a business. In this episode of The Data Hustle, we speak with David Cohen, founder of Superposition, a consultancy for data and AI consultancy founders, about what it actually takes to build and scale a boutique consultancy: positioning, pricing, go-to-market, hiring, discovery and scoping. David explains why most founders come to him after brute-forcing their way to a ceiling, why consultancies are bad at marketing and lose the attention game, and why his first piece of advice is simply to take more risks and be less boring. We get into pricing in depth: why hourly billing is easy but does not scale, how fixed, retainer and value-based models let you control margin, why the work costs what it costs regardless of where your team sits, and who ends up bearing the risk when a client's Terraform admin disappears for three weeks. We also discuss why a generic "we build insights and AI solutions" message reaches nobody, how to define an ideal customer profile and the personas inside it, how to qualify and say no to prospects, why it is okay to fire a client, and why a founder's first responsibility is to their team rather than to the client. Finally, David walks us through the white-labelled, gamified discovery workshops he runs for consultancies: getting the actual decision makers into one room, letting stakeholders fight it out early, and turning ambiguous client conversations into scoped work people actually buy. Find David: LinkedIn: https://www.linkedin.com/in/davcohen06/ Superposition: https://www.superpositionstrat.com/ Newsletter: https://superpositionstrat.beehiiv.com YouTube: https://www.youtube.com/@Superpositionstrat
Data Work is a Listening Job - #12 Data Hustle
Great data professionals do not just take requirements. They listen closely enough to understand the problem behind them. In this episode of The Data Hustle, we speak with Gabriela Costa, an analytics engineer and team lead at Indicium AI, about why communication and discovery are as important to good data work as SQL, modeling or engineering. Gabriela explains why immediately accepting “I need a dashboard” can send a data team in completely the wrong direction. Instead, she separates business discovery from data discovery: understanding how somebody works today, what decisions they are trying to make, what constraints they face and what problem they are actually trying to solve before deciding what should be built. We discuss treating dashboards and other analytical outputs as living data products rather than one-off projects, measuring adoption, uncovering requirements stakeholders themselves may not know they have, and what to do when your professional judgment tells you that the solution a stakeholder is asking for is the wrong one. We also get practical about stakeholder interviews. Gabriela shares how ideas from The Mom Test influence her discovery process, why scripted question lists can make junior consultants worse listeners, how she prepares teams for client conversations, and why understanding who actually owns a decision can prevent endless cycles of conflicting requirements. Later, we get into one of the more uncomfortable questions facing junior data professionals today: are people becoming too dependent on AI? Gabriela talks about analysts presenting code they cannot explain beyond “ChatGPT told me to do it,” and why using AI effectively still requires understanding the logic well enough to question, validate and defend what it produces. Finally, we talk about management, genuinely listening to the people you lead, and how Gabriela’s background in international relations shaped the way she approaches negotiation, different cultures, competing perspectives and consulting. Find Gabriela: LinkedIn: Gabriela Costa — @gabrieladados Indicium AI: indicium.ai
How Sequentum Scrapes the Web at Scale - #11 Data Hustle
AI is moving from generating answers to making decisions. That changes the stakes. In this episode of The Data Hustle, we speak with Sarah McKenna, CEO of Sequentum, about why AI needs the equivalent of seatbelts: clear guardrails, acceptance criteria, audit trails, deterministic workflows and accountable human review. Drawing on two decades across software test automation, DevOps, data quality and enterprise web extraction, Sarah explains why automation becomes fragile when teams optimise for speed without building in trust. We discuss reusable components, versioning, validation, low-code visibility, security reviews and the role of AI coding tools in maintaining large-scale data systems. We also zoom out to the growing conflict around the open web: publishers, AI crawlers, web-scraping companies, bot identification, paid data access and agentic commerce. Sarah explains why regulated organisations often reject AI at runtime, even while using it aggressively to accelerate development and strengthen governance. Find Sarah LinkedIn: https://www.linkedin.com/in/sarahransommckenna/ Sequentum: https://www.sequentum.com/
Taking Analytics by Storm with Agents - #10 Data Hustle
AI agents promise to let anyone analyse company data through a conversation—but reliable agentic analytics requires far more than connecting an LLM to a database. In this episode of The Data Hustle, we speak with Claire Gouze and Christophe Blefari, co-founders of Nao, about building an open-source framework for agentic analytics. They explain how their experience in consulting and data leadership exposed a recurring problem: business teams want answers faster, while data teams remain trapped as overloaded bottlenecks. We discuss which parts of data work should be automated, why analytics engineers may evolve into context engineers, whether traditional dashboards are becoming obsolete, and why companies risk replacing their dashboard graveyards with agent cemeteries. Claire and Christophe also break down the role of semantic layers, metadata and reusable context; why teams should not wait for perfect foundations before experimenting; how Nao structures its analytics agent; and how data professionals can use AI to become more visible and valuable rather than automate themselves out of the process. Find Nao at: - https://getnao.io - https://github.com/getnao/nao - https://www.linkedin.com/in/claire-gouze/ - https://www.linkedin.com/in/christopheblefari/
Data Superstars are a Trap - #9 Data Hustle
In this episode of The Data Hustle, we speak with Thomas in ’t Veld, CEO and co-founder of Tasman Analytics, about what makes a genuinely effective data consultant and how companies should build lean, capable data teams. Thomas explains why the strongest consultants combine technical depth, business understanding and communication skills—and why the best specialists still need a broad, T-shaped understanding of the wider data landscape. We also discuss how AI is changing recruitment, consulting and analytics delivery; what it really means to operate an AI-native data team; why strong data modelling and human-curated semantic layers still matter; and how small teams can use modern tools to produce the output that once required much larger departments. The conversation covers product thinking, context layers, data governance, stakeholder communication, consultancy career paths and the importance of focusing relentlessly on the business problems that actually move the needle. Find Thomas and Tasman at: Tasman Analytics: https://tasman.ai Thomas in ’t Veld: https://www.linkedin.com/in/thomasintveld/
Slow Data, Fast business - Ep8. Noel Gomez
In this episode of The Data Hustle, we sit down with Noel Gomez, co-founder of DataCoves, to discuss what it really takes to build and operate a modern data platform at scale. Having dbt, Apache Airflow, Snowflake and a collection of open-source tools does not automatically give you a platform. Noel explains why the difficult part is often the “glue” between those tools: governance, security, CI/CD, conventions, ownership and repeatable ways of working. We also explore what happens when AI dramatically accelerates data development. A team might go from 500 to 5,000 dbt models—but did it actually need those models? And who validates the output when AI is capable of being confidently wrong? Noel shares why AI should be treated as an efficiency tool rather than a replacement for human judgment, what junior data professionals should learn to remain valuable, and why curiosity, fundamentals and the ability to challenge AI matter more than simply generating more code. We discuss: * Why a collection of tools is not the same as a data platform * The challenge of running dbt and Airflow at enterprise scale * Why AI makes governance and conventions more important * How AI can accelerate technical debt * The risks of trusting AI-generated work without validation * What happens to junior engineers when companies automate entry-level work * Why data professionals need to understand the “why,” not only the implementation * Why strong teams go slower initially so they can move faster over time * How saying “no” prevents unnecessary products and features from becoming permanent liabilities Find Noel at: - https://www.linkedin.com/in/noelgomez/ - https://datacoves.com/
Kingsley Hall - AI-Enabled and Deeply Human
Kingsley Hall explores how AI is reshaping enterprise sales, why AI-enabled sellers will outperform traditional ones, and why storytelling, trust, and curiosity may become the real differentiators in a world full of AI-generated content.
Chris Gambill - Data Strategy Over Syntax
Chris Gambill, a Data Strategy and engineering leader with 25+ years of experience, talks about why strategy beats syntax, how aspiring data engineers can stand out in the AI era, and how senior professionals can move from implementation to trusted advisor.
Zharko Cekovski - Rescuing Broken Data Projects
Zharko Cekovski shares stories from rescuing broken data projects, where the real problems are organizational rather than technical, and offers practical advice on remote Data Consulting, Git as a collaboration skill, and when AI helps versus when it adds noise.
Shane Gibson - Beyond the Jira Ticket Factory
Shane Gibson breaks down how data teams can escape the Jira ticket factory, create more business value through better flow and stakeholder alignment, and what it takes to build a sustainable data Data Consulting practice.
Slawomir Tulski - Political Debt in Data Organizations
Sławomir Tulski, former Meta Data Engineering manager, digs into why data teams get stuck as ask factories, how political debt differs from technical debt, and what the AI era means for implementation-focused consultants.
Dr. Clair Sullivan - From Expert to Solopreneur
Dr. Clair Sullivan shares lessons on becoming a solopreneur, building credibility as a technical professional, and turning expertise into a sustainable Data Consulting business. Topics include pricing, networking, content, and how Gen AI is changing Data Consulting economics.
Thais Cooke - Data Analysts in the AI Era
Thais Cooke, senior healthcare data analyst and LinkedIn Learning instructor, talks about how AI is changing the analyst role, why SQL and critical thinking still matter, and why domain knowledge and stakeholder trust are becoming the real differentiators.
Guest Appearances
If you host a podcast about data or tech careers and want me on, I am always happy to join the conversation.
O stakeholder te pediu um Dashboard. Qual é o seu próximo passo?
Gabriela Costa and Juan Manuel Perafan discuss the importance of discovery in data projects. Rather than jumping directly to SQL or BI tools, professionals should first understand the stakeholder's underlying business problem.
Foundations of Analytics Engineer Role: Skills, Scope, and Modern Practices
In this episode, we discuss the Analytics Engineer role. What it includes and how it complements data engineers, data scientists, and business teams
From Psychology to Analytics Engineering
In this episode, Bolaji interviews Juan Manuel Perafan, a psychology major turned analytics engineer, who shares his journey from Colombia to the Netherlands and his transition into the data field.
Decifra lo que tu cliente quiere: Pongámosle datos al Marketing
En este episodio, Juan Manuel Perafan, Analytics Engineer en Xebia, comparte su experiencia en el mundo de los datos y cómo estos pueden transformar las estrategias de Marketing.
Data Governance for Analytics Engineering
Join Juan Manuel Perafan, Analytics Engineer at Xebia and Shinji Kim, Founder and CEO at Select Star, to discuss the fundamentals of Analytics Engineering and Data Governance that all Analytics Engineering teams need to consider.
From Psychology to Analytics
In this episode we have, Juan Manual Perafan, Analytics Engineer at Xebia who has more than 15 years of experience in the world of IT. We dive into his epic tech journey and learn about how he links Psychology with Tech.
Data Democratization
Embracing analytics throughout an organization can fuel innovation, business agility, and growth, making data integral to many modern business strategies.
Always taking the extra step
There's always someone who's taking the extra step to get where they want to go. And @juanalytics is that someone. He's been doing public speaking for 5 years.
Exploring The Design And Benefits Of The Data Platform
In this episode members of the GoDataDriven team, Guillermo Sanchez, Bram Ochsendorf, and Juan Perafan, explain the combinations of services that comprise this architecture, share their experiences working with clients to employ the stack, and the benefits of bringing engineers and business users together with data.
Versnel je data en haal poleposition met Tableau
Dutch webinar - discover how Tableau transforms Formula 1 data into powerful visualisations and race-winning insights.
SQL Lingua Franca
My first podcast (2024–2025): intimate conversations with knowledgeable guests about data, analytics engineering, and strategy.
Sarah Levy - Data Governance vs. AI
In this episode of SQL Lingua Franca, Sarah Levy, co-founder and CEO of Yuno, discusses data model governance, semantic layers, and the parallels between Data Governance
Dumky de Wilde - Hot Takes Special
Hot takes special episode with Dumky de Wilde
Lasse Benninga - Open Source Software in Data
In this podcast episode, the host introduces Lasse, an analytics engineer from Utrecht with a diverse background in software engineering and data and discusses the role of open source software in the data ecosystem.
Guillermo Sanchez - The Evolution of Analytics Engineering
In this episode of SQL Lingua Franca, we explore the dynamic shifts in analytics engineering over the past four years.
Thomas in't Veld - Data Maturity & The Future of Analytics
In this episode of SQL Lingua Franca, Thomas shares actionable strategies for helping companies advance their Data Maturity.
Mariam Halfhide - Responsible AI
In this episode, Mariam discusses the importance of responsible AI practices in today's data-driven world. She delves into the ethical considerations and governance frameworks necessary for implementing AI solutions that are both effective and socially responsible.
Ruben Huidekoper - Analytics Engineering
In this episode of the SQL Lingua Franca podcast, guest Ruben Huidekoper shares his journey as an analytics engineer. He talks about the broad scope of the role.
Pádraic Slattery - Mature Data Teams
In this episode, Padraic shares his background, starting from a degree in physics and transitioning into data engineering and Analytics Engineering. He discusses the challenges of building mature data teams.
Ksenia Iakovleva - KPIs & Django Girls
In this episode, Ksenia discusses her transition from journalism to data analysis in the financial sector. She shares insights on defining effective KPIs
Luuk Feitsma - Recruiting Data Professionals
In this episode, Luuk shares his diverse professional journey, starting in the music industry as a producer and DJ before transitioning into the IT recruitment
Daniel Herrera - Product Advocacy & GenAI
In this episode, we explore the global data landscape with Daniel Herrera, a developer and discuss why some GenAI tools succeed while others fail.
Tony Zeljković - Developer Experience'
In this episode, we explore the developer experience in data engineering with our guest, Tony. The conversation delves into strategie...
Ricardo Granados Lopez - Data Modeling
In this episode, we delve into the world of data careers and Analytics Engineering. Our guest, Ricardo Granados, shares his journey from med...
Fanny Kassapian - Data Transformations
In this episode, we dive into the world of data transformations. Our guest, Fanny Kassapian, shares insights on creating efficient, scalable, ...