Google · BigQuery
A warehouse your revenue team can actually use.
We design, build, and operate BigQuery warehouses that unify HubSpot, product, finance, and ad data, modeled cleanly, cost-controlled, and wired into the BI your team already runs on.
- Google Cloud partner
- dbt & SQLMesh modeling
- Cost-optimized architecture
- One warehouse
- Every revenue source unified
- dbt-modeled
- Version-controlled and tested
- 50%+
- Typical query-cost reduction
- Governed
- Access, PII, and lineage
The problem
Your data lives in ten places, and none of them agree.
Spreadsheet leadership
Every board meeting starts with someone reconciling HubSpot, Stripe, and NetSuite by hand.
Runaway BigQuery bills
Nobody owns query hygiene, partitioning, or storage lifecycle, and the invoice keeps growing.
No trusted metrics layer
Definitions live in dashboards, not in code. 'ARR' means five different things depending on who's asking.
PII everywhere
Sensitive data replicated across tables with no access controls or lineage.
The solution
A modeled, governed warehouse, built to scale.
Source-of-truth ingestion
Fivetran, Airbyte, or custom pipelines from every revenue-critical system into raw layers in BigQuery.
dbt / SQLMesh models
Staging, marts, and metrics layers in version control, with tests, docs, and lineage.
Cost & performance controls
Partitioning, clustering, materialized views, and slot management to keep spend flat as you grow.
Governance & access
Role-based access, column-level security, and PII policies aligned with SOC 2 and GDPR.
What's included
The full analytics stack, built to your data.
PILLAR 01
Ingestion & pipelines
Every source, reliably landed.
- Fivetran, Airbyte, and custom loaders
- HubSpot, Stripe, Segment, GA4, Ads, product
- CDC-based replication where supported
- Backfill and reconciliation tooling
PILLAR 02
Modeling & metrics
Business logic in code.
- dbt or SQLMesh project bootstrap
- Staging, intermediate, and marts architecture
- Semantic metrics layer for BI
- Testing, docs, and CI/CD
PILLAR 03
Cost & performance
Predictable spend as data grows.
- Partitioning and clustering strategy
- Materialized views and BI Engine tuning
- Slot reservations and query monitoring
- Storage-lifecycle rules
PILLAR 04
Governance
Safe to open up.
- IAM, dataset, and column-level access
- PII tagging and masking policies
- Data-catalog and lineage tooling
- Audit logs and retention policy
Process
Five stages. Warehouse-live in weeks.
- 01
Audit
Inventory sources, current tooling, and reporting needs. Sizing and cost model for BigQuery.
- 02
Architect
Warehouse design, modeling framework, governance model, approved before build.
- 03
Build
Pipelines, dbt project, and metrics layer built in a dev environment with full test coverage.
- 04
Validate
Reconciliation against source systems, stakeholder sign-off, and BI cutover.
- 05
Operate
Monitoring, cost review, and quarterly modeling roadmap.
READY WHEN YOU ARE
Get one warehouse. One truth.
Tell us where your revenue data lives today. We'll come back with a BigQuery architecture and cost model within one business day.