Without tested models, every dashboard is an opinion disguised as a metric.
When data pipelines are fragile, untested, and undocumented, every downstream consumer inherits the fragility.
Your analysts spend 80 percent of their time cleaning data
That is an engineering problem, not a people problem. Manual CSV exports, VLOOKUP chains, and copy-paste pipelines do not scale. They break.
Finance and marketing disagree on revenue
The CEO does not know who to believe. Untested data models produce contradictory truths. The problem is never the dashboard, it is always the model.
GA4 exports a new field and your pipeline fails silently
Last week’s dashboard shows stale data. Nobody notices until the Friday report. Without data contracts and tests, fragility is guaranteed.
The same dbt architecture powers Ubisoft and Sezane
Starter kit deploys in 3 to 5 days. Full custom modeling for complex businesses: 2 to 4 weeks.
Medallion architecture
Raw, Staging, Intermediate, Marts. Version-controlled in git, tested with CI/CD, documented with lineage.
BigQuery optimization
Partitioning by query pattern, clustering, BI Engine reservations. Fast and cost-controlled.
Automated data quality
dbt tests, source freshness checks, anomaly alerting. Broken pipelines caught before they reach a dashboard.
Semantic layer KPIs
One definition of revenue. One definition of a customer. Teams query trusted metrics, not raw tables.
BigQuery designed for your access patterns.
We set up your BigQuery project with partitioning, clustering, cost controls, and IAM governance, architected for the queries your team actually runs.
What you get:
- BigQuery project with cost controls and budget alerts
- Partitioning and clustering by access pattern
- BI Engine reservation for sub-second dashboards
- IAM governance with least-privilege access per role
Medallion architecture from raw data to trusted marts.
Raw-Staging-Intermediate-Mart layers. Incremental models for 100 to 200 times compute reduction. Jinja macros for DRY logic. SQLFluff linting in CI/CD.
What you get:
- Medallion: Raw to Staging to Intermediate to Marts
- Incremental models: 100 to 200 times compute reduction
- Jinja macros and dbt_utils for reusable logic
- SQLFluff linting and CI/CD on every model change
One definition of revenue. One definition of a customer.
Trusted business metrics defined once and used everywhere. No more spreadsheet wars between teams.
What you get:
- Semantic layer built on tested dbt marts
- Centralized KPI definitions with lineage
- Self-serve analytics for every stakeholder
- Version-controlled metric logic
Automated testing that catches problems before they reach any report.
dbt tests, source freshness, anomaly detection, and data contracts. Your warehouse stays trustworthy at scale.
What you get:
- Automated dbt tests on every model
- Source freshness monitoring
- Anomaly detection pipelines
- Data contracts between teams
From raw data to trusted source of truth.
Warehouse assessment
Audit current BigQuery setup, query patterns, and cost leaks. Quantify where fragility lives.
Medallion design
Raw, Staging, Intermediate, Marts architecture mapped to your business logic and access needs.
Modeling and testing
Incremental dbt models built, tested with CI/CD, and documented with full lineage.
Semantic layer deployment
Trusted KPIs defined once. Self-serve analytics enabled for every team.
Handover and governance
Team training, monitoring setup, and runbooks. Your team owns and evolves the warehouse autonomously.
“100 to 200 times compute reduction. One single source of truth. Dashboards that teams actually trust.”
Tested, documented, production-ready infrastructure
Concrete deliverables your team can actually use. No slide decks, only tested artifacts.
BigQuery Warehouse
Fully optimized project with partitioning, clustering, cost controls, and IAM governance.
dbt Project
Complete medallion architecture with incremental models, tests, and CI/CD pipeline.
Semantic Layer
Centralized, version-controlled KPI definitions used by every dashboard and report.
Data Quality Framework
Automated testing, anomaly alerting, and source freshness monitoring built in.
Documentation and Lineage
Full dbt docs with lineage graphs. Every metric traceable to its source.
Team Runbook
Training materials and governance guide so your team owns the warehouse from day one.
9 dbt playbooks. Same architecture used at Ubisoft and Sezane.
Every model follows documented patterns refined across real enterprise deployments. No guesswork.
Is this the right engagement for you?
Best fit
- Companies with growing data volumes and multiple stakeholders
- Teams tired of manual spreadsheet pipelines
- Organizations that need trusted KPIs across marketing, finance, and product
- Brands preparing for self-serve analytics at scale
Not ideal for
- Teams without any data warehouse yet
- Companies with very low data volume
Common questions
Do you replace our existing warehouse or build on top?
We audit first. If the foundation is salvageable, we harden and extend it. If not, we rebuild cleanly and migrate data safely.
How long does a full analytics engineering engagement take?
Starter medallion kit deploys in 3 to 5 days. Full custom modeling for complex businesses takes 2 to 4 weeks.
Can we maintain the dbt project after handover?
Yes. You receive a complete git repo, CI/CD setup, and detailed runbooks. Our Intelligence retainer is available for ongoing support.
Certified Analytics Engineer
DataBird 2025
GA4 Certified
GTM Server-Side Specialist
BigQuery Certified
Google Cloud
Enterprise Track Record
Carrefour. Airbus. Club Med. Ubisoft
Luxury and Premium Clients
Sezane. ByRedo. Valrhona
50 plus Client Engagements
Artefact. Sleekery. MadMetrics
Your warehouse is full of potential. Is it delivering trust?
Book a diagnostic. We run a quick audit and show you exactly where fragility lives and how fast we can fix it.