From Claude to Pipelines: AI-Assisted Data Engineering

AI-assisted data engineering is transforming how developers work—but current data platforms weren't built for this workflow. Unlike software development where Claude Code excels with fast, safe iteration on local files, data engineering involves cloud runtimes, distributed systems, and shared production storage. Most legacy platforms rely on point-and-click interfaces—not AI agents writing code. In this live demo, we showcase how Bauplan brings the same Git-native workflow that makes Claude Code effective directly to data engineering. Bauplan's "Git-for-Data" architecture enables isolated data branches, atomic merges, and safe experimentation from your terminal using simple Python and CLI commands. We walk through importing S3 data into Iceberg tables, AI-generated data quality reports, building transformations in isolated branches, and publishing to production—all orchestrated by Claude Code through Bauplan's Skills framework. No Spark clusters, no Kubernetes. Just clean Python code that Claude can read, write, and debug. 0:00 The AI Assistant Problem in Data Engineering 5:52 Working with code VS working with Data 12:45 Live Demo: Claude Code + Write-Audit-Publish Workflow 37:12 Q&A 43:01 Closing Remarks **Resources:** Learn more: https://www.bauplanlabs.com Claude Code: https://claude.ai