How to build a successful data career
Data careers are among the fastest-growing roles across global industries. With expectations shifting due to AI and cost pressure, staying career-ready can be more challenging than ever.
In this peer exchange, we will draw on our collective experience in competency matrices, cross-industry transitions, and personal branding to offer a practical roadmap for building a lasting career. Through group activities and conversation, we will explore questions like “Which data skills feel most durable as AI changes expectations for the role?”, “What interview question helped you avoid a bad fit?”, and “How do you make your impact visible and transferable across teams or companies?” This peer exchange is a practical, story-driven pattern swap for building career momentum without defaulting to “just become a manager.”
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What AI agents get wrong, and how semantics fixes it
Sonny Rivera / ThoughtSpotView session - Breakout session
From DML soup to dbt structure: Ecolab's migration story
Tomasz Kaczmarek / EcolabChiel Fernhout / DatafoldView session - Lightning talk
Getting the most out of dbt on Databricks
Srilekha Dornadula / DatabricksShubham Dhal / DatabricksView session
