Generative AI Agent Foundations on AWS for Data Engineers

Description

This one-day, instructor-led virtual course teaches experienced engineers how to bring generative AI agents into data engineering workflows on AWS. Participants learn what AI agents are, how to build them using Amazon Bedrock, how orchestration patterns coordinate multi-step agent workflows, and how the Model Context Protocol (MCP) connects agents to external tools and data sources. The course also covers agent governance and management practices, and demonstrates how GitHub Copilot in VS Code accelerates coding and testing for data engineering tasks on AWS infrastructure. All labs use data engineering scenarios — ETL pipelines, data quality checks, schema management, and pipeline orchestration — so participants apply every concept to the work they do every day.

Who This Course Is For

• Data Engineers with 8+ years of experience building and maintaining data pipelines
• Data Architects designing data platform infrastructure across cloud providers
• Senior Engineers and Engineering Leads evaluating AI agent adoption for their teams
• Participants with backgrounds in AWS, Azure, and/or GCP (course content is AWS-focused but concepts are portable)

What You Will Learn

• Explain generative AI fundamentals and identify where AI agents can add value to data engineering workflows on AWS.
• Design and build foundational AI agents using Amazon Bedrock Agents with tool use for data engineering tasks.
• Implement orchestration patterns to coordinate multi-step agent workflows for data pipeline scenarios.
• Build and configure MCP servers that expose data engineering tools and resources to AI agents through a standardized protocol.
• Apply governance and management practices to ensure AI agents are secure, auditable, and compliant for production data engineering use.
• Use GitHub Copilot in VS Code to accelerate coding, testing, and debugging for data engineering tasks on AWS infrastructure.

Prerequisites

• 8+ years of hands-on experience in data engineering, architecture, or platform engineering
• Familiarity with at least one major cloud provider (AWS, Azure, or GCP)
• Comfort with Python or similar scripting language
• Basic understanding of data pipelines, ETL processes, and data quality concepts
• An AWS account with access to Amazon Bedrock (model access must be enabled before the course)
• VS Code installed with the GitHub Copilot extension activated
• A modern web browser and reliable internet connection for vILT delivery

Delivery Options

• Virtual instructor-led