The AI-Native Data Engineer

Data engineering is a hard job — and AI is making it harder. More data sources, more consumers, more coding agents writing SQL across your estate, and a backlog that never shrinks. Eight in 10 organizations have deployed AI-based data engineering tools, yet engineers are managing more complexity than ever: 55% cite data security and privacy as their biggest challenge, according to an MIT Technology Review Insights report.

Most teams deploy AI tools to make existing processes run faster. The real opportunity is AI-native data engineering — rethinking the processes themselves.

This ebook shows you how to build toward autonomous data engineering using Snowflake CoCo, the data-native coding agent that works across your entire data estate. Each chapter is a standalone walkthrough of a core pipeline workflow, grounded in modern engineering practices — version control, automated testing and CI/CD — that make automation trustworthy.

You'll learn how to:

Get started with CoCo and the fundamental skills for AI-native pipeline work

Connect your entire data estate with catalog-linked databases, without moving data

Ingest continuously with change data capture and Openflow

Serve data at low latency with streaming pipelines

Transform at scale with dbt and build Python pipelines with Snowpark

Migrate Apache Spark™ workloads to Snowflake

Manage infrastructure declaratively with DCM Projects

Read it straight through or jump to the workflow you're building today. Either way, you'll finish with the building blocks for every layer of the data engineering lifecycle — and a clear path toward autonomous data engineering.