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The engineers who lead the next two years will be the ones architecting smarter systems, not writing more scripts.\u003C/h4\u003E\r\n\u003Cp\u003EThe questions are real and coming fast: What should your data engineering org look like when agentic AI is writing pipelines? How do you build infrastructure that scales with AI's appetite for fresh, diverse data? When does declarative design beat imperative orchestration? And what does it mean to deliver data products when your consumers include AI agents?\u003C/p\u003E\r\n\u003Cp\u003EIn “Build Pipelines for AI: An Essential Guide to Smarter Data Engineering,” \u003Cb\u003EGilberto Hernandez\u003C/b\u003E offers a clear-eyed look at where data engineering is headed and what it takes to get ahead of it. Featuring a foreword by \u003Cb\u003EChris Child\u003C/b\u003E, VP of Product for Data Engineering at Snowflake, this ebook walks through the full pipeline lifecycle — ingestion, transformation, delivery and DataOps — showing you the modern, declarative tools built to handle AI's demands at every stage.\u003C/p\u003E\r\n\u003Cp\u003EData engineers are becoming the operational architects of AI-forward organizations. This book is designed to help you build for that future, today.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EWHAT YOU'LL LEARN\u003C/b\u003E\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003EHow AI is reshaping ingestion, transformation, delivery and DataOps — and the modern approaches, like Cortex Code, that address each shift\u003C/li\u003E\r\n\u003Cli\u003EWhy declarative pipelines scale well for AI workloads, and how to make the transition using Snowflake DCM Projects\u003C/li\u003E\r\n\u003Cli\u003EHow to deliver data products that serve both human consumers and AI agents, including semantic layers built for accurate AI querying in Snowflake Intelligence\u003C/li\u003E\r\n\u003Cli\u003EThe DataOps practices — version control, CI/CD, environment management and AI-assisted development — that let your team iterate at AI speed\u003C/li\u003E\r\n\u003Cli\u003EWhere the data engineer's role is headed as agentic AI takes on more of the pipeline, and how to position your team to lead it\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EABOUT THE AUTHOR\u003Cbr\u003E\r\nGilberto Hernandez is a Lead Developer Advocate at Snowflake, where he helps data engineers build modern, AI-native pipelines. 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