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 billing system that nearly cost us our biggest customers, to a churn prediction model gone haywire because of the smallest change. These are around real incidents, fixes and hard-won lessons that changed how teams worked together.

You'll walk away with practical mental models, real tooling patterns, and practical next steps you can take to bridge the gap between Data and Engineering.

You'll learn:

• Why shared ownership of data quality matters more than better tooling and how to actually build it
• Practical patterns that work today: data contracts and schema registries, observability patterns applied to data and how to deal with the messy reality of production data
• What "T-shaped" really means for senior engineers in the AI era - the specific skills and knowledge that give you leverage when it comes to dealing with data systems