Build the data backbone that powers modern AI systems in this hands-on course that combines cloud data engineering, automation, and generative AI integration. Learners gain a comprehensive understanding of how data pipelines enable intelligent systems-developing skills in data ingestion, transformation, orchestration, and optimization across modern cloud ecosystems. Through guided labs and projects, learners work with tools such as Python, Airflow, Kafka, dbt, Spark, and Terraform to design, deploy, and manage scalable data workflows. Special emphasis is placed on linking enterprise data to Generative AI tools using vector databases and retrieval-augmented generation (RAG) techniques. The course culminates in a capstone project: building and presenting a fully automated AI-powered data pipeline deployed in a cloud environment. Along the way, learners gain real-world experience managing infrastructure as code, monitoring pipeline performance, and ensuring security, governance, and cost efficiency. Graduates leave with the technical expertise and automation mindset to build reliable, production-ready data systems that enable AI at scale.