Production-Ready AI for Data Engineering: Context, Trust & Automation

Behind every great LLM application lies exceptional data engineering. As generative AI shifts from "vibe coding" to autonomous systems, the primary production bottleneck is no longer raw model capability, but trust, context management, and robust engineering. This session explores the evolution of GenAI in data engineering—from simple snippets to AI-native architectures—and details how engineering gaps are filled with intentional data strategies.
We dive into the "plumbing" of the token economy, demonstrating how categorizing activities and engineering context through tools like agent.md and claude.md can yield 95%+ accuracy. Attendees will discover advanced context strategies, including repository graph indexing to cut token usage and model-agnostic skill toolkits (MCP) for seamless integration across Gemini, Claude, and GPT.
The session concludes with a practical case study of a complex Snowflake pipeline. By poking holes in AI analysis and validating queries, we demonstrate actionable frameworks to optimize token spend, improve output accuracy, and reduce development time for AI-driven data pipelines.