​Everyone has an opinion about what AI is doing to data engineering and on the 1st of October we'll discuss what actually made it into production and what hasn't.

​Three engineers will talk about successful and less successful AI initiatives they've experienced recently.

​Data Basecamp is a gathering for people who work with data hands on. Data engineers, analytics engineers, platform teams, ML folks.

​Sessions

​Session 1: Data Engineering In The Era Of The Software Factory

​Ben Rogojan @ SeattleDataGuy

​After over a decade of data engineering, I’ve seen more than a few hype cycles come and go. Every one promised to dramatically shift how businesses used data. They promised that businesses would be at a massive competitive advantage if they could harness their data and become data-driven. Over the past few years, we’ve been going through yet another cycle with more promises. So what has changed and what hasn’t? Are companies actually finding value by increasing their data engineering and analytical outputs?

Data teams are no longer just responsible for moving data from point A to point B or migrating from one warehouse to another. They are increasingly responsible for creating the context, non dashboard data interfaces, and infrastructure that both humans and AI systems depend on. In this talk, I’ll discuss what I’m seeing across startups, SMBs, and large enterprises as they adopt AI-assisted development and move toward software-factory-style workflows.

​Session 2: Evergreen Governance: Data Strategy That Grows With AI

​Nathan Erbe, Senior Data Leader + Advisor, Denver

​Data governance is often treated as a compliance checkbox - necessary, but static. This session makes the case that governance is actually evergreen: the same core discipline (classification, access control, quality, monitoring) that's always mattered now has to extend into a new surface area: LLM pipelines, agentic query layers, and model outputs that drive real decisions.

​Drawing on real-world healthcare AI implementations, from a claims-based ML model that triggers patient outreach to an LLM pipeline that ingests sensitive physician contracts, this talk offers a practical framework (Classify > Control > Monitor > Adapt) for extending governance into AI without starting from scratch. Attendees will leave with concrete next steps they can apply to their own data and AI initiatives this quarter.

​Session 3: Migrating in Stereo: How Agentic Workflows Tackled Two System Migrations at Once

​Dan D'Orazio, Principal Data Engineer @ Kin Insurance

​Why migrate one system at a time when you can let AI agents handle two simultaneously? Leveraging multi-agent architectures opened up a world of possibilities for system migrations that struggled to get off the ground.

​This talk walks through the technical architecture, execution, and hard-earned lessons of orchestrating simultaneous system migrations using agentic AI. We’ll discuss where autonomous agents shine in complex environments, where they struggle, and how to build guardrails that keep your data intact. Spoiler alert: a human-in-the-loop was integral.

​Schedule

​5:30 Doors, food and drinks
6:00 Talks begin
7:00 Open networking
8:00 Close

​Presented by: estuary.dev