Services
Analytics Engineering

One source of truth. Zero spreadsheet wars.

Every team has its own spreadsheet. Every spreadsheet tells a different story. We build the warehouse where revenue and customer mean one thing, and every dashboard pulls from the same tested source.

EDF
Club Med
Ubisoft
Stellantis
PSG
Airbus
Deezer
Orange Bank
Sezane
ByRedo
Valrhona
Servier
SoRare
Qonto
Louis Poulsen
KILROY
Tropic Skincare
Unbottled
Nutrimuscle
Witt Group
Ubigi
Inwi
Naya
Nabogo
Atma
What breaks without data engineering

If your teams cannot agree on the numbers, nothing else matters.

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.

This is how

Architecture refined across enterprise and luxury e-commerce

Starter kit deploys in 3 to 5 days. Full custom modeling for complex businesses: 2 to 4 weeks.

01

Data pipeline architecture

Four layers from raw data to trusted metrics. Version-controlled, tested, documented.

02

BigQuery optimization

Optimized for the queries your team actually runs. Fast dashboards, controlled costs.

03

Automated data quality

dbt tests, source freshness checks, anomaly alerting. Broken pipelines caught before they reach a dashboard.

04

Unified business metrics

One definition of revenue. One definition of a customer. Teams query trusted metrics, not raw tables.

Services

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
  • Sub-second dashboard performance
  • IAM governance with least-privilege access per role
  • Fivetran, Supermetrics, Adverity for source ingestion

Data pipeline architecture from raw data to trusted marts.

Raw-Staging-Intermediate-Mart layers. Incremental models that process only new data, not the full history. Reusable logic patterns. Automated quality checks on every change.

What you get:

  • Data Pipeline: Raw to Staging to Intermediate to Marts
  • Incremental models that process only new data, not the full history
  • Reusable logic patterns for consistent metric definitions
  • Automated quality checks and code review on every model change
  • Dimensional modeling for regulatory and compliance reporting

One definition of revenue. One definition of a customer.

Trusted business metrics defined once and used everywhere. No more spreadsheet wars between teams. Compatible with Omni, Looker, and dbt Semantic Layer.

What you get:

  • Unified metrics built on tested data models
  • 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
  • Technical debt reduction and pipeline optimization
Our process

From raw data to trusted source of truth.

01

Warehouse assessment

Audit current BigQuery setup, query patterns, and cost leaks. Quantify where fragility lives.

02

Pipeline design

Raw, Staging, Intermediate, Marts architecture mapped to your business logic and access needs.

03

Modeling and testing

Incremental dbt models built, tested with CI/CD, and documented with full lineage.

04

Semantic layer deployment

Trusted KPIs defined once. Self-serve analytics enabled for every team.

05

Handover and governance

Team training, monitoring setup, and runbooks. Your team owns and evolves the warehouse autonomously.

One source of truth for revenue, customers, and performance. Warehouse costs cut through incremental processing. Teams query trusted metrics, not raw exports.
MadMetrics track recordBigQuery and dbt deployments
What you receive

Tested, documented, production-ready infrastructure

Concrete deliverables your team can actually use. No slide decks, only tested artifacts.

01

BigQuery Warehouse

Fully optimized project with partitioning, clustering, cost controls, and IAM governance.

02

dbt Project

Complete data pipeline architecture with incremental models, tests, and CI/CD pipeline.

03

Semantic Layer

Centralized, version-controlled KPI definitions used by every dashboard and report.

04

Data Quality Framework

Automated testing, anomaly alerting, and source freshness monitoring built in.

05

Documentation and Lineage

Full dbt docs with lineage graphs. Every metric traceable to its source.

06

Team Runbook

Training materials and governance guide so your team owns the warehouse from day one.

Behind this service

9 dbt playbooks. Same architecture used at Ubisoft and Sezane.

Every model follows documented patterns refined across real enterprise deployments. No guesswork.

9dbt modeling guides
4BigQuery optimization playbooks
50+models deployed
Fit

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
  • Organizations with regulatory or compliance reporting requirements (finance, banking)
  • Teams needing to partner data with non-technical stakeholders

Not ideal for

  • Teams without any data warehouse yet
  • Companies with very low data volume
FAQ

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 data pipeline 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.

Certifications and Track Record

The receipts.

Get in touch

Certified Analytics Engineer

DataBird 2025

GA4 Certified

Google

GTM Server-Side Specialist

Google

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.