DataFramed

AI agents are starting to handle parts of the shopping journey that used to require human judgment — discovery, comparison, checkout. But behind every agent recommendation is a massive, invisible layer of data infrastructure. Product catalogs need to be structured, inventory synced in real time, pricing accurate, and quality signals clear. For data engineers and teams building at companies like Shopify, this shift means rethinking how data flows through systems and what "good enough" quality actually means. How do you ensure data is ready for AI? And how is this reshaping what data teams actually do?
Nell Thomas is the VP of Data at Shopify, where she leads a team of approximately 400–500 people across data infrastructure, ML platforms, data engineering, and data science. Her career spans multiple industries including social media (Facebook), e-commerce (Etsy), politics (Hillary for America, Democratic National Committee), and now commerce. She holds an A.B. in Psychology from Harvard University and an M.A. in History & Sociology of Science from the University of Pennsylvania.
In the episode, Richie and Nell explore agentic commerce and how AI agents are transforming shopping, the role of data in enabling AI-driven commerce, Shopify's Catalog and Universal Commerce Protocol, data quality requirements for agentic systems, how the data team function is evolving at Shopify, changing skill requirements for data professionals, and Nell's unconventional career path from politics to tech.
Links Mentioned in the Show:
- Agentic Commerce on Shopify
- Universal Commerce Protocol (UCP) vs Agentic Commerce Protocol (ACP)
- Shopify Catalog Documentation
- Agentic Storefronts — Shopify Sales Channel
- ChatGPT — OpenAI's Conversational AI
- How Shopify Built Data Infrastructure at Scale
Related
Scaling Data Quality in the Age of Generative AI
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Technical skills are being commoditized faster than ever. As AI takes on more of the work that used to define a junior knowledge worker, the things that once made someone valuable are becoming table stakes. What compounds in this environment is reputation — what colleagues, clients, and decision-makers think about you when your name comes up.
That puts new pressure on visibility. People doing great work in silence are increasingly the ones getting passed over for promotions and external opportunities. So how do you build a reputation without becoming an influencer? What does AI-era credibility actually look like? And how do you start small?
Dorie Clark teaches Executive Education at Columbia Business School and is the Wall Street Journal and USA Today bestselling author of The Long Game, Entrepreneurial You, Reinventing You, and Stand Out. She has been named four times as one of the Top 50 business thinkers in the world by Thinkers50, recognized as the #1 Communication Coach in the world by the Marshall Goldsmith Leading Global Coaches Awards, and is a frequent contributor to the Harvard Business Review.
In the episode, Richie and Dorie explore why AI fluency is the new Excel skill, tinkering with AI's jagged frontier, the security risks of agentic AI, what personal branding really means in an AI-disrupted job market, the recognized expert formula, the ladder strategy for credibility, networking with "no asks for a year," running better meetings, and much more.
Links Mentioned in the Show:
• The Jagged Frontier (HBS Working Paper)
• Agentic Misalignment: How LLMs could be insider threats (Anthropic)
• AI-powered coding tool wiped out a software company's database (Fortune)
• Reinventing You by Dorie Clark
• The Long Game by Dorie Clark
• Superteams by Ron Friedman
• Connect with Dorie on LinkedIn
• AI-Native Course: Intro to AI for Work
• Related Episode: #341 Our Data Trends & Predictions for 2026
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The role of the machine learning engineer is being rewritten in real time. AI coding assistants are absorbing parts of the day-to-day, planning and evaluation are eating up more of the week, and the lines between machine learning engineer, AI engineer, and data scientist are blurrier than ever. For anyone working in data and AI — or trying to break in — this shift changes what skills are worth investing in, what employers actually screen for, and how interviews are run. What's still worth learning? What does a competitive portfolio look like? And how do you stand out when a thousand applicants are using bots to apply?
Marina Wyss is a Senior Applied Scientist at Twitch (an Amazon company), where she builds production AI and machine learning systems across content understanding, recommendations, and forecasting. She came into the field from a non-traditional background — a political science undergrad and a Master's in social data science in Berlin — and has held machine learning roles at Coursera and a Berlin-based statistical consultancy along the way. Outside her day job, Marina runs a popular AI/ML YouTube channel and weekly newsletter, and coaches people transitioning into machine learning from non-traditional careers.
In this episode, Richie and Marina explore how AI is reshaping the machine learning engineer role, the shifting balance between coding and planning, why evaluation matters more than ever, the differences between ML engineer, AI engineer, and data scientist roles, how to break into the field from a non-technical background, what makes a strong portfolio project, the hiring process at big tech, how to prepare for technical interviews, networking strategies that actually work, what success looks like in your first few months on the job, and much more.
Links Mentioned in the Show
• Chip Huyen — AI Engineering (book)
• Andrew Codesmith on YouTube
• Phillip Choi on YouTube
• A Life Engineered on YouTube
• Keras
• LeetCode
• Connect with Marina: LinkedIn
• AI-Native Course: Intro to AI for Work
• Related Episode: How to Have a Career in Data Science in 2025 with Dawn Choo
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Every conversation about AI in data eventually arrives at the same question: which roles survive, and which ones get automated away? Generative AI can already draft SQL, build dashboards, and run exploratory analysis — but it still can't sit with a business stakeholder and untangle what "customer" actually means across five teams. For data professionals, that shifts the day-to-day from production work toward translation, modeling, and judgment. So which skills are worth doubling down on? Which roles are becoming central, and which are quietly disappearing? And what should anyone hiring — or being hired — be paying attention to right now?
Veronika Durgin is the VP of Data at Saks Global, where she leads data strategy across the luxury retail group. A full-stack data executive with more than two decades of experience spanning database administration, data engineering, platform architecture, data modeling, and analytics, Veronika is a Snowflake Data Superhero and a member of CDO Magazine's Global Editorial Board. She writes about data modeling, data culture, and data leadership on her Substack and Medium.
In the episode, Richie and Veronika explore the future of data careers under AI, why analytics engineering becomes the catch-all role, the skills and hiring shifts data leaders are making, centralized data with decentralized analytics, keeping enterprise data teams agile, conceptual data modeling as the unglamorous prerequisite to AI, semantic layers, agentic commerce, and much more.
Links Mentioned in the Show:
Connect with Veronika: LinkedIn
Veronika's Substack: Think. Solve. Repeat.
dbt — referenced as the origin of "analytics engineering"
Open Data Science Conference (ODSC) — Veronika's recent talk on data and company politics
Amazon "two-way door" decisions — Bezos shareholder letter
Jessica Talisman — Veronika's recommendation for knowledge graphs and ontologies
Juan Sequeda — referenced on semantic layers and knowledge graphs
Catalog & Cocktails podcast (hosted by Juan Sequeda)
AI-Native Course: Intro to AI for Work
Related Episode: Creating an AI-First Data Team with Bilal Zia, Head of Data Science & Analytics at Duolingo

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Most AI ethics conversations sound the same: be fair, be transparent, be accountable. The values are right, but in practice they don't get teams out of bed in the morning. Executives nod along, employees take the compliance training, and meanwhile real risks like hallucinations, cascading failures, and autonomous agents acting at scale slip through. So what shifts when teams stop chasing an ethical ideal and start naming the specific disasters they want to avoid? Who needs to be in the room to spot them? And what kind of training actually changes how people use AI day to day?
Reid Blackman is the founder and CEO of Virtue, an AI ethical risk consultancy, and the author of The Ethical Nightmare Challenge: How to Avoid the Worst of AI (2026) and Ethical Machines (HBR Press, 2022). A former philosophy professor at Colgate with a PhD from the University of Texas at Austin, he has designed responsible AI programs for organizations including Amazon, Etsy, Kraft Heinz, Merck, US Bank, and Nationwide, and has advised the FBI, NASA, the World Economic Forum, and the Canadian government on federal AI regulations. He also hosts the Ethical Machines podcast.
In the episode, Richie and Reid explore why responsible AI fails to motivate organizations, the biggest AI ethical nightmares facing companies today, the unique risks of agentic AI including cascading failures and emergent risks, the Ethical Nightmare Challenge framework, cross-functional ENC teams, training employees in plain language, scaling AI governance, measuring success by what you avoid, and much more.
Links Mentioned in the Show:
• The Ethical Nightmare Challenge by Reid Blackman
• Ethical Machines by Reid Blackman
• Ethical Machines podcast
• Claude Code
• Connect with Reid: LinkedIn
• AI-Native Course: Intro to AI for Work
• Related Episode: #350 How to Make Hard Choices in AI with Atay Kozlovski
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