AI-assisted development can generate a data pipeline in seconds. The harder question is whether the generated pipeline reflects the decisions your data actually requires.

A syntactically correct pipeline can still introduce subtle problems: incorrect joins, duplicated records, broken incremental logic, inappropriate transformations or assumptions about what a business metric means.

In this session, we will examine what changes when AI becomes part of the data engineering workflow. Through practical examples, we will look at where AI-assisted development can accelerate pipeline development, where it tends to make dangerous assumptions, and how developers and data engineers can introduce review, testing and validation into the workflow.

Rather than presenting AI as a replacement for data engineering expertise, the session focuses on a more useful model: AI handles more of the implementation, while engineers become increasingly responsible for specifying intent, validating results and catching the mistakes that code review alone may not reveal.

Attendees will leave with practical patterns for using AI-assisted development without turning data pipelines into code that is fast to generate but difficult to trust.