Data Engineering Podcast
Data Engineering Podcast

Branches, Diffs, and SQL: How Dolt Powers Agentic Workflows

01 February 2026 56:53 Tobias Macey

Listen to episode

About this episode

Summary
In this episode Tim Sehn, founder and CEO of DoltHub, talks about Dolt - the world’s first version‑controlled SQL database - and why Git‑>
Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • If you lead a data team, you know this pain: Every department needs dashboards, reports, custom views, and they all come to you. So you're either the bottleneck slowing everyone down, or you're spending all your time building one-off tools instead of doing actual data work. Retool gives you a way to break that cycle. Their platform lets people build custom apps on your company data—while keeping it all secure. Type a prompt like 'Build me a self-service reporting tool that lets teams query customer metrics from Databricks—and they get a production-ready app with the permissions and governance built in. They can self-serve, and you get your time back. It's data democratization without the chaos. Check out Retool at dataengineeringpodcast.com/retool today and see how other data teams are scaling self-service. Because let's be honest—we all need to Retool how we handle data requests.
  • Your host is Tobias Macey and today I'm interviewing Tim Sehn about Dolt, a version controlled database engine and its applications for agentic workflows

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you describe what Dolt is and the story behind it?
  • What are the key use cases that you are focused on solving by adding version control to the database layer?
  • There are numerous projects related to different aspects of versioning in different data contexts (e.g. LakeFS, Datomic, etc.). What are the versioning semantics that you are focused on?
  • You position Dolt as "the database for AI". How does data versioning relate to AI use cases?
  • What types of AI systems are able to make best use of Dolt's versioning capabilities?
  • Can you describe how Dolt and Doltgres are implemented?
  • How have the design and scope of the project changed since you first started working on it?
  • What are some of the architecture and integration patterns around relational databases that change when you introduce version control semantics as a core primitive?
  • What are some anti-patterns that you have seen teams develop around Dolt's versioning functionality?
  • What are the most interesting, innovative, or unexpected ways that you have seen Dolt used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on Dolt?
  • When is Dolt the wrong choice?
  • What do you have planned for the future of Dolt?

Contact Info

  • LinkedIn

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

  • Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
  • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
  • If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.

Links

  • Dolt
  • DoltHub
  • Stockmarket Data
  • LakeFS
  • Datomic
  • Git
  • MySQL
  • Prolly Tree
  • Neon
  • Django
  • Feature Store
  • MCP Server
  • Nessie
  • Iceberg
  • PlanetScale
  • O(NlogN) Big O Complexity
  • B-Tree
  • Git Merge
  • Git Rebase
  • AST == Abstract Syntax Tree
  • Supabase
  • CockroachDB
  • Document Database
  • MongoDB
  • Gastown
  • Beads

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 Data Engineering Podcast. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.