BigQuery is often the largest line item in a GCP bill; yet for many organizations, it remains a black box.
When costs spike, the questions follow:
Who ran that query?
Why did this dashboard cost $40K?
Why is usage outpacing revenue?
As AI initiatives expand, platforms like BigQuery are becoming more central — and more heavily used. Cost visibility and governance are no longer just FinOps concerns; they’re strategic priorities.
In this session, Senior Cloud Architect Sayle Matthews (“The BigQuery Dude”) explains how BigQuery pricing works in practice — where costs originate, how they escalate, and where they typically break down.
Through real-world examples, he’ll show how to move from reacting to month-end surprises to building a FinOps-driven model with shared visibility across engineering and finance.
What you'll learn:
Identify who and what drives BigQuery spend
Implement guardrails that prevent runaway queries
Turn billing data into actionable insight for smarter technical and financial decisions
This session is for:
Data platform leaders, cloud architects, and CloudOps and FinOps professionals responsible for managing, optimizing, or forecasting BigQuery spend; especially in organizations scaling analytics and AI workloads.
Sayle Matthews is a Senior Cloud Architect III (Data) at DoiT specializing in BigQuery architecture and cost optimization. Known as “The BigQuery Dude,” he partners directly with enterprise teams to resolve performance challenges, control spend, and implement practical FinOps strategies across modern data platforms.
