The role of the Data Engineer is rapidly evolving in the age of AI and large language models.
This seminar explores how data engineering is shifting from traditional data pipelines to building intelligent, AI-ready systems and workflows.
We’ll cover key principles for designing modern data platforms, and look at how AI interacts with the data layer through use cases such as natural language querying (NLQ), semantic models and AI-assisted data engineering.
We’ll also dive into architectures such as Retrieval-Augmented Generation (RAG) and examine how agents and tool-calling patterns are transforming data workflows.
Along the way, we’ll address critical considerations including security, cost management and observability in AI-driven data systems—supported by practical insights, live demonstrations and real-world use cases.
