Your Snowflake, Databricks, or Azure bill doubled and nobody can point to what changed.

The situation

Cloud data platforms make it trivially easy to spend money and nearly impossible to see where it went. A single mis-scheduled job, an auto-suspend that never fires, or a SELECT * in a dashboard that refreshes every five minutes — any of these can quietly add thousands to your monthly bill. Finance is asking questions and the honest answer is “we’re not sure.”

It rarely stops at warehouse compute. A Power BI semantic model built the slow way can force you onto a bigger, far more expensive Fabric or Premium capacity than the workload actually needs. Databricks clusters left as always-on, all-purpose compute instead of auto-terminating job clusters quietly bill around the clock. And a dbt project that grew without anyone watching the modeling patterns — redundant transformations, everything full-refreshed instead of incremental — can turn into a steady, invisible drain on Snowflake credits.

What the Audit does

I pull your query history, warehouse and cluster usage, Power BI/Fabric capacity metrics, and dbt project metadata, and follow the money. Where the waste is clean and measurable — an idle warehouse, an oversized cluster — you get a number backed directly by usage data. Where the fix is structural — a dbt DAG that needs untangling, a semantic model that needs rebuilding — you get a reasoned estimate and the logic behind it, not a number I can’t back up. You’ll always know which kind of number you’re looking at.

What you walk away with

• A spend map attributing cost to workloads and query patterns, so you can see exactly where the money’s going.
• A ranked savings list, split into confirmed dollar savings and reasoned estimates — highest impact and lowest effort first in each.
• Guardrails (budgets, resource monitors, query policies) so the same leak doesn’t reopen in three months.

A few examples

This isn’t guesswork from a single playbook — recent engagements have covered:

• Azure Analysis Services — right-sized a model off its most expensive tier (S9v2, roughly $26K/month at list price) down to S4, cutting that single line item by about $10K/month with no perceptible drop in report performance.
• Databricks — replaced always-on, all-purpose clusters with correctly sized, auto-terminating job clusters, and consolidated 50+ legacy ingestion notebooks into a single reusable framework.
• Snowflake / dbt — cut wasteful CI rebuilds that were burning warehouse credits on every pull request.
• Azure SQL Database & VMs — moved to elastic pools and auto-scaling sized to actual business-hours load, with automated pause/resume outside those hours.
• Data Factory — consolidated redundant, copy-pasted pipelines built up over years into a reusable linked-service and dataset framework.

Individually, each of these has produced a real, measurable drop in a specific bill. Collectively, on one platform alone, they add up to a five-figure sum in recurring savings every month.