Blurring the Lines: Engineering & Data Teams in the Age of AI

Every senior engineer knows the feeling: a model makes a bad decision, a customer complains, and suddenly you're debugging a system that spans three teams, two pipelines, and a machine learning model nobody fully owns. Where do you even start?

The boundary between engineering and data has been dissolving for years and AI is making it collapse. Data engineers write infrastructure code. Backend engineers serve ML predictions. Analysts ship production logic. The old world of "engineering builds apps, data builds dashboards" is gone, and what's replaced it is messier, more interesting and full of opportunity for engineers willing to look beyond their own layer of the stack.

In this talk, I'll share real stories from building data and engineering systems - from a broken event tracking system that undermined every product decision, to a credit eligibility pipeline where only analysts could explain why a customer got rejected. These are around real incidents, fixes and hard-won lessons that changed how entire teams worked together.

You'll walk away with a clear, honest picture of what senior engineers actually need to understand about data systems and AI/ML in 2026 - and, just as importantly, what you can safely ignore. No "just become a data scientist" hand-waving. Just practical mental models, real tooling patterns, and a framework for being the kind of engineer who can reason across the whole stack with confidence.

You'll learn:

• Why shared ownership of data quality matters more than better tooling and how to actually build it
• How to debug across the engineering/data/ML boundary when something breaks in production
• Practical patterns that work today: data contracts and schema registries, LLM-based spec validation and observable data infrastructure
• What "T-shaped" really means for senior engineers in the AI era - the specific knowledge that gives you leverage and the stuff that doesn't