Solutions
Analytics Engineering
Turn raw warehouse tables into tested, documented, version-controlled models with agreed metric definitions, so reporting and AI both read from the same source.
Inside the delivery
Identify source grain, keys, refresh schedules and the owners who can explain changes. The flow below shows the main delivery stages and the evidence produced at each step.
Identify source grain, keys, refresh schedules and the owners who can explain changes.
Output
Source model and dependency map
Build staging and business models with explicit joins, history handling and naming conventions.
Output
Versioned transformation project
Add uniqueness, relationship and business-rule tests; agree metric definitions with stakeholders.
Output
Model tests and metric dictionary
Review model changes, validate downstream dependencies and monitor scheduled runs.
Output
Deployment workflow and model documentation
Controls across the workflow
Before you commit
Overview
Two dashboards disagreeing about revenue is not a BI problem. It is what happens when transformation logic lives inside individual reports instead of in a shared, tested modeling layer. Analytics engineering puts that logic under version control, adds tests to it, and gives every metric one definition and one owner.
Capabilities
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Design the layers between raw ingestion and consumption so the warehouse stays understandable as the number of sources grows.
Treat data models like application code, with automated tests and documentation generated from the same source as the models.
Agree what each metric means, where it is defined, and who owns the definition, then keep reporting tools reading from that definition.
Put data changes through the same release discipline as software, so a model change is deployed rather than edited in production.
Integrations
Tools are chosen around your existing systems, access requirements and operating constraints.
Frequently asked questions
Analytics engineering focuses on tested transformations and business definitions inside the analytical platform. Source connectivity and ingestion infrastructure belong to data engineering when they need separate work.
Data engineering is responsible for getting data into the platform reliably. Analytics engineering is responsible for what happens to it afterwards: the modeling, testing, and metric definitions that turn landed tables into something reporting and AI can trust.
No. dbt is the common tool and we work with it often, but the practice is the point: transformation logic in version control, tested, documented, and released rather than edited in place. The same discipline can be applied with other tooling.
Usually, yes. Disagreeing numbers almost always trace back to the same calculation being written separately in several reports. Moving that logic into a shared, tested model layer and repointing the reports is the fix.
Yes. Analytics engineering sits on top of the warehouse you already have. We typically start with the highest-traffic metrics, model those properly, and migrate reporting across incrementally rather than pausing reporting for a rebuild.
An analytics agent inherits whatever ambiguity exists in the tables beneath it. Documented models, agreed metric definitions, and tested transformations are what let you evaluate an AI answer against a known-correct one.
Warehouse access, source schemas, existing transformations, reporting dependencies and business owners for disputed metrics.
The agreed delivery includes deployment workflow and model documentation. Review model changes, validate downstream dependencies and monitor scheduled runs.
We review the available inputs before estimating: Warehouse access, source schemas, existing transformations, reporting dependencies and business owners for disputed metrics. The proposal identifies dependencies, review milestones and excluded work; the scope determines the schedule.
Related
Start a conversation
Share your current situation and the constraint you need to resolve. We will use the discovery inputs above to define a practical scope for Analytics Engineering.