Modernizing a Fortune 500 Europe data platform — and cutting cloud spend by $10K+/month
Architecting and modernizing a large enterprise data platform on Snowflake, Databricks, and Azure — and the infrastructure and dbt optimizations that cut cloud spend by $10K+/month.
The situation
A Fortune 500 Europe enterprise data platform had grown to well over 4,000 dbt models — spanning models, tests, snapshots, and seeds — running across Snowflake, Databricks, and Azure, with the whole environment provisioned as code via Terraform. At that scale, small inefficiencies compound fast: an oversized cluster here, a full-refresh model that should be incremental there, and the cloud bill quietly becomes impossible to explain.
What changed
- Consolidated 50+ legacy Databricks ingestion notebooks into a single templated framework, improving throughput and data quality while cutting maintenance overhead.
- Identified and implemented infrastructure optimizations — Snowflake credit usage, job-cluster sizing, CI efficiency, and dbt modeling optimizations (incremental builds over full refreshes, trimming redundant transformations) — that reduced cloud spend by $10K+/month.
Why it matters
None of this required ripping anything out. The pattern that keeps showing up: platforms don’t get expensive or fragile all at once — they accumulate small, reasonable-at-the-time decisions until nobody can hold the whole picture in their head. The fix is usually architectural, not a bigger warehouse.
Client details anonymized under contractual confidentiality. Figures and scope are accurate to the engagement.