In this episode, Lucas talks with Arthur Wang of Babylist about how a hackathon project became the Data Doula analytics agent, why analysts now own the semantic layer, and how his team decides which changes deserve human attention when AI reviews the routine ones.

Arthur Wang is a Principal Data Engineer at Babylist, where he builds AI-native platforms and tools, such as the Data Doula analytics agent, that make data accessible and trustworthy. He's worked as an analyst and engineer in tech startups, retail, utilities, and consulting as both an IC and leader. He lives in Portland, OR with his wife and daughter.

What you will learn

- How a data team of 20 to 25 divides the work between engineers and analysts

- Why data roles are becoming both broader and deeper as AI takes on more tasks

- How to decide what to buy and what to build in an AI analytics stack

- Advice for early-career data professionals on slowing down and focusing on what moves the needle

Resource mentioned

Arthur's blog post on building Data Doula, from the Babylist Engineering blog

https://www.babylist.com/engineering/blog/data-doula

Chapters

0:00 Welcome and guest intro

0:31 Arthur's path into data and Babylist

2:59 Using fulfillment data to cut costs and improve customer experience

7:11 Data team size and the engineer to analyst ratio

10:14 Building the Data Doula analytics agent from a hackathon

13:32 How data engineer and analyst roles are getting broader and deeper

16:08 Institutional knowledge and the cost of maintaining an analytics agent

18:19 Buying versus building, and avoiding vendor lock-in

21:54 The future of work and moving up a level of abstraction

23:36 Why attention matters more than time

25:10 Letting AI review low-risk changes so humans focus on what matters

27:35 Next projects and keeping the semantic layer from drifting

30:12 Advice for a 25-year-old data professional

31:00 Closing thoughts

In this episode, Lucas talks with Frank Tingley, Founder and Principal Advisor at Tome Advisory, about the analyst's path from answering requests to owning data products, why small gains compound over time, and why executives should spend three to five percent of their time engaging with the data. Frank has spent more than a decade in data analytics, across Fortune 500 companies and high-growth direct-to-consumer brands.

What you will learn

• How segmenting products across sales channels lifted dot-com revenue by about five percent
• Why analysts are becoming full-stack
• Why context management matters more as AI automates hard skills like SQL and dashboard building
• What to look for when hiring analysts in the age of AI
• Why the 95th percentile of data work is not good enough

Resource mentioned 95%-ile isn't that good, by Dan Luu https://danluu.com/p95-skill/

Chapters

0:00 Welcome and guest intro

0:31 Career path from finance to analytics and advisory

4:01 Where analytics has driven impact

4:44 A channel segmentation example and how AI shifts the work

8:49 Team structure and the full-stack analyst

13:15 Context management and why hard skills matter less

15:42 How to hire and interview analysts in the age of AI

17:44 Advice for a 25-year-old analyst

18:25 Compounding returns in analytics

23:07 Data-first culture and the urgency of every week

25:40 Why the 95th percentile is not good enough

29:54 What executives should do to engage with data

34:25 Closing thoughts

In this episode, Lucas talks with Ratheesh Kamoor of Warner Bros. about two decades in tech, from the dot-com bust to building data platforms for direct-to-consumer subscription products. They cover what makes a data foundation ready for AI agents, how to structure data teams, and where data careers are heading.

Ratheesh Kamoor is a technology executive and AI, Data & Analytics leader with 20+ years of experience building and scaling data-driven organizations, platforms, and intelligent solutions for global businesses. He specializes in bridging technology, data science, and business strategy to transform complex data into actionable intelligence, accelerate innovation, and drive measurable business outcomes.

Ratheesh currently serves as an executive at Warner Bros. Discovery, leading Data & AI initiatives that advance customer intelligence, analytics, and AI-powered business transformation. Prior to WBD, he held leadership positions at Disney and multiple startups, building high-performing teams and delivering data and technology capabilities at scale.

A passionate advocate for AI, generative AI, and open-source technologies, Ratheesh is focused on translating emerging innovations into practical business solutions that improve decision-making, unlock growth, and create lasting value.

What you will learn

• Why slow data is almost no data, and how lakehouse and lambda architectures help
• How to extend an existing data and ML platform so agents can run on it
• When a centralized, hub and spoke, or hybrid data team fits best
• How data engineer, analytics engineer, and product engineer roles are changing
• Career advice for early-career technologists in the age of AI

Chapters

0:00 Welcome and guest intro

0:17 From the dot-com bust to building data platforms

4:29 Why streaming was an early home for AI

5:44 Building a data foundation for AI agents

9:51 Centralized data platforms and a single semantic layer

16:24 How data roles are changing in the age of AI

21:32 Business and product skills, and managing agents

23:35 Career advice for early-career technologists

26:30 Is AI making everyone a manager?

28:36 More startups, more building, and bigger productivity gains