GCP engineering: BigQuery data platforms, Cloud Run services, and Vertex AI workloads on the cloud built around data and machine learning.
Data platforms that outgrow their shortcuts
BigQuery makes it trivial to start querying and expensive to keep querying badly: unpartitioned tables scanned end to end, scheduled queries nobody owns, and per-query costs that spike with growth. A modeled, governed data platform costs a fraction to run and is the difference between analytics and archaeology.
What we build with Google Cloud
- BigQuery data platforms modeled with dbt and governed for cost
- Cloud Run services and event-driven processing with Pub/Sub
- Vertex AI pipelines for model training and serving
- GCP cost and architecture reviews of existing environments
How we work
Partition and cluster from the first table, not the first big bill
Model the warehouse in dbt so metrics stay consistent
Prefer serverless services; pay for traffic, not idle
Wire IAM and project structure before teams multiply
Typical stack
Frequently asked questions
When analytics or ML is the core of the workload, BigQuery and Vertex AI are hard to beat. For general-purpose infrastructure the three big clouds are closer, and existing commitments usually decide.
Almost always. Partitioning, clustering, materialized models, and moving hot dashboards off raw tables typically cut scan volumes dramatically. We audit query patterns first and fix the expensive ones by design, not by policing analysts.