Data Modeling & Transformation
- Dimensional and mart models built in dbt, versioned in Git
- Raw, landed data turned into clean, analysis-ready tables
- Transformation logic your team can read, extend, and review
AnalyticsEngineering
We turn raw data into tested, documented datasets your whole company can trust.
Capabilities
Data Quality & Testing
- dbt tests on every model, run on every change
- Freshness, uniqueness, and referential checks at scale
- Broken numbers caught in CI, not in a board meeting
Semantic Layers & Metrics
- One definition of revenue, churn, and every metric that matters
- Semantic layers that serve BI and AI from the same source of truth
- Metric changes reviewed and versioned like code
Self-Service Enablement
- Documented, discoverable datasets non-technical teams can query
- Catalogs and lineage that answer where a number came from
- Fewer ad hoc requests landing on your engineers
BI & Dashboard Enablement
- Dashboards built on tested models, not fragile SQL
- Reporting that reconciles across teams and tools
- Marketing, product, and finance served from one modeled core
Why
When revenue means three different things in three different dashboards, every meeting starts with an argument about whose number is right. We end that argument.
Every metric gets one tested, documented definition that lives in code, in version control, with a review process behind it.
Finance, product, and marketing pull from the same modeled core, so the numbers reconcile before anyone hits refresh.
dbt is the industry standard for a reason, and we have been building with it since before it was one. Staging, marts, tests, docs: the full discipline, not just the tool.
Models are versioned in Git, reviewed like application code, and tested on every change, so a bad merge never makes it to a dashboard.
And we leave behind naming conventions and a project structure your next analytics hire will thank us for.
Most self-service rollouts end with the data team still answering the same questions, just with more tools in the mix. Structure is what fixes that.
We document the datasets, wire up lineage, and organize the catalog so people can find the right table and trust what they find.
Your analysts stop being a query desk and get back to analysis.
The same clean models and shared definitions that make dashboards trustworthy are exactly what make AI useful. RAG and GenAI retrieve from tested, documented data instead of guessing.
We build the metadata, lineage, and semantic layers that let models find the right numbers and show where they came from.
That is the quiet payoff of analytics engineering done right: you end up one integration away from AI features, not one rebuild away.
Process
Audit
A clear map of your models, metrics, and gaps, with a modeling plan worth building.
- Stakeholder interviews
- Warehouse & dbt project review
- Metric inventory & gap analysis
Build
A tested, documented dbt layer your analysts can query with confidence.
- Dimensional modeling in dbt
- Automated testing & CI/CD
- Documentation & lineage
Operate
A self-service analytics practice your team runs without us.
- Team training & handoff
- Metric reviews & governance
- Fractional support on retainer
In their words
Aeolus Data Solutions provided us with an early prototype that solved our analytical needs, and set up the foundation for building future pipelines. Their work was essential in enabling us to make data-driven decisions as we scale.Hiring Aeolus consultants was easily the best decision given how much experience they brought to our growing team.We highly value Aeolus Data Solutions' professional recommendations when we migrated from GCP to Databricks. They evaluated the requirements and growth projections before advising.Aeolus helped us scope, plan and build out the data and analytics foundation that scales. DE is an ever-changing industry and Aeolus seems to always know the best solution to our specific problem.