Ra Raman

AI agents can build pipelines, generate dbt models, evolve Snowflake schemas and write quality tests. But, they lack three pieces essential for production-grade data engineering demands: 1) context about your data stack and business, 2) governance to enforce access controls, policies and cost constraints, and 3) infrastructure to validate changes before they hit your warehouse. Pradnesh Patil, Co-Founder of Altimate.ai, shares the missing stack for agentic data engineering. Watch agents catch schema drift, self-correct hallucinated models and enforce guardrails in Snowflake and across the entire data platform.

Managing data at scale and efficiency requires a delicate balance of governance and collaboration. Learn how Zscaler’s enterprise data team built its governance framework using dbt Cloud and Snowflake to automate best practices, optimize SQL, and improve data quality across thousands of dbt models and hundreds of business dashboards. Discover how CI/CD processes, lineage tracking and dashboard health scoring can enhance trust and empower teams to develop and govern models faster.

Zscaler is transforming customer intelligence with a powerful platform that aggregates data from numerous sources to provide a 360° view of customer accounts. Leveraging next-gen reports and ML models, they deliver deep account insights—helping account teams identify risks, prevent churn, and drive customer engagement. Powered by an enterprise chatbot with agentic workflows, these insights enable real-time, data-driven decisions. Join us to explore the future of AI-driven customer intelligence!