In this episode, I sit down with Mark Freeman and Chad Sanderson (Gable.ai) to discuss the release of their new O’Reilly book, Data Contracts: Developing Production-Grade Pipelines at Scale. They dive deep into the chaotic journey of writing a 350-page book while simultaneously building a venture-backed startup. The conversation takes a sharp turn into the evolution of Data Contracts. While the concept started with data engineers, Mark and Chad explain why they pivoted their focus to software engineers. They argue that software engineers are facing a "Data Lake Moment, "prioritizing speed over craftsmanship, resulting in massive technical debt and integration failures.

Gable: https://www.gable.ai/

Modern enterprises can’t manage data they don’t understand – uncovering the code-to-data relationship is the missing link. As data ecosystems grow more complex, traditional approaches to tracking data lineage can’t keep up. This talk explores how AI-driven code analysis can automatically build end-to-end lineage graphs, giving engineers clear visibility into hidden dependencies across large, legacy, and regulated systems. We’ll show how AI enhances data catalogues and introduce Gable - a tool that helps teams map, validate, and monitor data flows at scale. A live demo on a large energy data codebase will highlight how AI transforms lineage tracking from a manual headache into an automated, scalable solution.

Panel: How AI Is Shifting Data Infrastructure Left | Joe Reis, Vin Vashishta, Carly Taylor, Chad Sanderson | Shift Left Data Conference 2025

The rapid rise of AI has dramatically elevated the value and strategic importance of data, transforming how upstream software engineers perceive and interact with data workflows. In this expert-led panel, industry leaders will share their experiences and insights into effectively bridging the gap between data teams and software engineers. They will discuss practical strategies for proactively managing data infrastructure, enhancing collaboration, and ensuring high-quality data to support advanced AI-driven development initiatives.

Panel: Shift Left Across the Data Lifecycle—Data Contracts, Transformations, Observability, and Catalogs | Prukalpa Sankar, Tristan Handy, Barr Moses, Chad Sanderson | Shift Left Data Conference 2025

Join industry-leading CEOs Chad (Data Contracts), Tristan (Data Transformations), Barr (Data Observability), and Prukalpa (Data Catalogs) who are pioneering new approaches to operationalizing data by “Shifting Left.” This engaging panel will explore how embedding rigorous data management practices early in the data lifecycle reduces issues downstream, enhances data reliability, and empowers software engineers with clear visibility into data expectations. Attendees will gain insights into how data contracts define accountability, how effective transformations ensure data usability at scale, how proactive how proactive data and AI observability drives continuous confidence in data quality, and how catalogs enable data discoverability, accelerating innovation and trust across organizations.

Panel: State of the Data And AI Market | Apoorva Pandhi, Matt Turck, Chris Riccomini, Chad Sanderson | Shift Left Data Conference 2025

Artificial Intelligence is reshaping the landscape of software development, driving a fundamental shift towards empowering developers to take control earlier in the development lifecycle—known as "shift left." In this panel, venture capital leaders and industry experts will explore how emerging trends in AI and data technologies are influencing investment decisions, creating new opportunities, and transforming development workflows. Attendees will gain valuable insights into the evolving market dynamics, understand the strategic significance of shifting left in today's AI-driven world, and discover how organizations and developers can stay ahead in this rapidly changing environment.

Data DevOps applies rigorous software development practices—such as version control, automated testing, and governance—to data workflows, empowering software engineers to proactively manage data changes and address data-related issues directly within application code. By adopting a "shift left" approach with Data DevOps, SWE teams become more aware of data requirements, dependencies, and expectations early in the software development lifecycle, significantly reducing risks, improving data quality, and enhancing collaboration.

This session will provide practical strategies for integrating Data DevOps into application development, enabling teams to build more robust data products and accelerate adoption of production AI systems.

In an era where data drives decision-making and innovation, data engineering stands at the forefront of technological advancement. 

This panel brings together leading experts; Chad Sanderson, Joe Reiss, Sarah Levy and Pushkar Garg to explore the critical challenges and opportunities shaping the field today.

In this session, Chad Sanderson, CEO of Gable.ai and author of the upcoming O’Reilly book: "Data Contracts," tackles the necessity of modern data management in an age of hyper iteration, experimentation, and AI. He will explore why traditional data management practices fail and how the cloud has fundamentally changed data development. The talk will cover a modern application of data management best practices, including data change detection, data contracts, observability, and CI/CD tests, and outline the roles of data producers and consumers. 

Attendees will leave with a clear understanding of modern data management's components and how to leverage them for better data handling and decision-making.

In this session, Chad Sanderson, CEO of Gable.ai and author of the upcoming O’Reilly book: "Data Contracts," tackles the necessity of modern data management in an age of hyper iteration, experimentation, and AI. He will explore why traditional data management practices fail and how the cloud has fundamentally changed data development. The talk will cover a modern application of data management best practices, including data change detection, data contracts, observability, and CI/CD tests, and outline the roles of data producers and consumers. Attendees will leave with a clear understanding of modern data management's components and how to leverage them for better data handling and decision-making.

ABOUT THE TALK: Data Contracts are a mechanism for driving accountability and data ownership between producers and consumers. Contracts are used to ensure production-grade data pipelines are treated as part of the product and have clear SLAs and ownership.

Learn about the why, when and how of Data Contracts and the spectrum from culture change to implementation details.

ABOUT THE SPEAKER: Chad Sanderson is the former Head of Data at Convoy. He has implemented Data Contracts at scale on everything from Machine Learning models to Embedded Metrics. He currently operates the Data Quality Camp Slack group.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai/

WARNING: This episode contains detailed discussion of data contracts. The modern data stack introduces challenges in terms of collaboration between data producers and consumers. How might we solve them to ultimately build trust in data quality? Chad Sanderson leads the data platform team at Convoy, a late-stage series-E freight technology startup. He manages everything from instrumentation and data ingestion to ETL, in addition to the metrics layer, experimentation software and ML.  Prukalpa Sankar is a co-founder of Atlan, where she develops products that enable improved collaboration between diverse users like businesses, analysts, and engineers, creating higher efficiency and agility in data projects.  For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

Today I chat with Chad Sanderson, Head of Product for Convoy’s data platform. I begin by having Chad explain why he calls himself a “data UX champion” and what inspired his interest in UX. Coming from a non-UX background, Chad explains how he came to develop a strategy for addressing the UX pain points at Convoy—a digital freight network. They “use technology to make freight more efficient, reducing costs for some of the nation’s largest brands, increasing earnings for carriers, and eliminating carbon emissions from our planet.” We also get into the metrics of success that Convoy uses to measure UX and why Chad is so heavily focused on user workflow when making the platform user-centered.

Later, Chad shares his definition of a data product, and how his experience with building software products has overlapped with data products. He also shares what he thinks is different about creating data products vs. traditional software products. Chad then explains Convoy’s approach to prototyping and the value of partnering with users in the design process. We wrap up by discussing how UX work gets accomplished on Chad’s team, given it doesn’t include any titled UX professionals. 

Highlights:

Chad explains how he became a data UX champion and what prompted him to care about UX (1:23) Chad talks about his strategy for beginning to address the UX issues at Convoy (4:42) How Convoy measures UX improvement (9:19) Chad talks about troubleshooting user workflows and it’s relevance to design (15:28) Chad explains what Convoy is and the makeup of his data platform team (21:00) What is a data product? Chad gives his definition and the similarities and differences between building software versus data products (23:21) Chad talks about using low fidelity work and prototypes to optimize solutions and resources in the long run (27:49) We talk about the value of partnering with users in the design process (30:37) Chad talks about the distribution of UX labor on his team (32:15)

Quotes from Today’s Episode  

Re: user research: "The best content that you get from people is when they are really thinking about what to say next; you sort of get into a free-flowing exchange of ideas. So it’s important to find the topic where someone can just talk at length without really filtering themselves. And I find a good place to start with that is to just talk about their problems. What are the painful things that you’ve experienced in data in the last month or in the last week?" - Chad 

Re: UX research: "I often recommend asking users to show you something they were working on recently, particularly when they were having a  problem accomplishing their goal. It’s a really good way to surface UX issues because the frustration is probably fresh." - Brian 

Re: user feedback, “One of the really great pieces of advice that I got is, if you’re getting a lot of negative feedback, this is actually a sign that people care. And if people care about what you’ve built, then it’s better than overbuilding from the beginning.” - Chad

“What we found [in our research around workflow], though, sometimes counterintuitively, is that the steps that are the easiest and simplest for a customer to do that I think most people would look at and say, ‘Okay, it’s pretty low ROI to invest in some automated solution or a product in this space,’ are sometimes the most important things that you can [address in your data product] because of the impacts that it has downstream.” - Chad 

Re: user feedback, “The amazing thing about building data products, and I guess any internal products is that 100% of your customers sit ten feet away from you. [...] When you can talk to 100% of [your users], you are truly going to understand [...] every single persona. And that is tremendously effective for creating compelling narratives about why we need to build a particular thing.” - Chad 

“If we can get people to really believe that this data product is going to solve the problem, then usually, we like to turn those people into advocates and evangelists within the company, and part of their job is to go out and convince other people about why this thing can solve the problem.” - Chad 

Links: Convoy: https://convoy.com/ Chad on LinkedIn: https://www.linkedin.com/in/chad-sanderson/ Chad’s Data Products newsletter: https://dataproducts.substack.com

Bayesian vs. Frequentist. False Positive vs. False Negative. Truth vs. Uncertainty. It's the world of A/B testing! In this bonus mini-episode, Moe sat down with Chad Sanderson from Subway to discuss some of the pitfalls of A/B testing -- the nuances that may seem subtle, but are anything but trivial when it comes to planning and running a test.