Marketing and finance report different revenue numbers for the same month because each built their own query.
Service
Analytics-ready data your whole company can trust.
We build the transformation layer between raw data and your dashboards — tested models, consistent metric definitions, and documentation — so two teams pulling the same number get the same answer.
Modeling flowActive
- Metrics & source auditDefine what 'revenue' actually means
- 02Data modelingBuild tested transformation layers
- 03Metric layer & documentationOne definition, everywhere
- 04BI enablementConnect dashboards to trusted models
- 05Validation & handoffTests, docs, ownership
Why teams come to us
What this service helps you solve
Your dashboards sit directly on raw tables, so every schema change breaks a report somewhere.
New hires can't tell which of a dozen similarly-named tables is the source of truth.
You need consistent, tested metric definitions before scaling self-serve analytics.
Deliverables
What’s included
Metrics and source data auditData modeling (staging, intermediate, mart layers)Automated testing on transformation modelsCentralized metric and definition layerBI tool integration and dashboard supportDocumentation of models and definitionsData quality monitoringStatement of work before billing
Process
How the service works
- 01
Audit raw sources and existing metric definitions
- 02
Build tested, layered transformation models
- 03
Centralize metric definitions in one place
- 04
Connect BI tools to the trusted modeling layer
- 05
Document models and hand off or continue support
Roles
Roles that may support this service
Analytics EngineerData EngineerBI DeveloperData Analyst
Final roles are recommended after discovery.Discovery before SOW
Discovery comes before every reliable statement of work.
Before we recommend roles, timelines, or pricing, we need to understand your goals, technology stack, product situation, scope, risks, and constraints. Discovery helps us align expectations and create a realistic statement of work.
What discovery documents
- Business goals
- Product goals
- Technology stack
- Current situation
- Required roles
- Timeline expectations
- Budget expectations
- Risks and unknowns
- Success criteria
FAQ
Questions about this service
Data Engineering builds the underlying platform and warehouse. Analytics Engineering builds the transformation and metrics layer on top of it, the part that turns raw tables into something an analyst or dashboard can trust. Many clients need both, and discovery tells us how much.
If your dashboards query raw tables directly, they're fragile and prone to inconsistent numbers across teams. We insert a tested modeling layer underneath so the dashboards you already have become reliable, without necessarily rebuilding them.
We typically build with dbt on top of your existing warehouse, but we scope the toolset during discovery based on what your team already knows and maintains.