GCP
GCP programs that fit data and ML—IAM hierarchy, networking, and services leveraged with clear FinOps guardrails.
What you get with Quipus GCP
We design around projects/folders and VPC patterns that isolate blast radius while keeping shared services usable. BigQuery/GKE/Vertex choices tie to data gravity and team skills.
Billing hygiene: budgets, labels, and recommendations—so growth doesn’t surprise finance.
GCP pillars
Org & IAM
Hierarchy, groups, and service accounts with least privilege.
- Workload identity
- Org policies
- VPC SC
Data & ML
Pipelines and notebooks with lineage and secure access.
- BigQuery
- Dataflow
- Vertex
Platform ops
GKE best practices, autoscaling, and observability integrations.
- Config Connector
- Cloud Ops
- SLOs
Key elements of our GCP process
Senior practitioners ship GCP in tight loops with demos, quality gates, and visibility—so your team can steer without surprises.

We wire instrumentation, feedback, and review rituals around GCP so decisions reflect real usage in your product—not assumptions.

Documentation, enablement, and clear ownership so GCP keeps delivering value after the engagement—your org stays in control.

What GCP can unlock
Data-forward cloud
Architectures that suit analytics and ML without bespoke glue.
Cleaner IAM stories
Hierarchical permissions reduce sprawl and mistakes.
Cost-aware growth
Labels and budgets make spend attributable and actionable.

GCP with Quipus: what we offer
Landing zone
Project structure, networking, and baseline security.
Workload migration
Plans and execution from on-prem/other clouds with validation.
FinOps
Commitments, rightsizing, and cleanup programs.
Enablement
Patterns and training for developers and data teams.
Answers to CommonQuestions
Clear answers about GCP within Cloud & Platform Engineering—how we scope work, what we need from you, and how engagements typically run.