Leadership asks you to cut the cloud warehouse bill by 40 percent without hurting the business. What do you do?

What the interviewer wants to hear

• Measure first: attribute cost by warehouse, user, query and pipeline
• Remove waste: unused tables, idle compute, duplicate or over-frequent jobs
• Optimise heavy queries: partition pruning, clustering, fewer columns, incremental models
• Right-size compute with auto-suspend, sizing and workload isolation
• Tune storage: retention, time travel, lifecycle tiers
• Add guardrails: budgets, alerts, quotas and cost ownership

Model answer

I would not guess; I would measure. Using query history and billing data, I attribute spend to warehouses, service accounts, dbt models and dashboards, and usually a handful of jobs drive most of the cost. Quick wins come from waste: warehouses that never suspend, jobs running hourly when daily is enough, full-refresh models that could be incremental, and tables or dev copies nobody reads. Next I tune the heaviest queries: filter on partition and cluster columns so pruning works, stop SELECT * on wide tables, pre-aggregate dashboard sources, and fix exploding joins. I right-size compute, separate workloads so a big batch job does not require a huge shared warehouse, and set aggressive auto-suspend. For storage I review time travel and retention and move cold data to cheaper tiers. Finally I add budgets, alerts and per-team cost dashboards so the savings last.

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