Data Strategy & Architecture
- Data & AI-readiness audits that end in a build-ready blueprint
- Warehouse and lakehouse design on Snowflake, Databricks, or BigQuery
- Platform and tooling choices sized to your stage, not the vendor's
DataEngineering
We create a data-driven culture that influences everyday decision-making.
Capabilities
Pipeline Engineering
- Batch and streaming ETL/ELT with Airflow, dbt, Spark, and Kafka
- Version-controlled, tested, and CI/CD-deployed from day one
- Fragile scripts replaced with pipelines that hold in production
Migration & Modernization
- Legacy ETL, database, and warehouse moves to the modern stack
- Source-to-target validation so nothing silently breaks
- Cloud cost optimization baked into the move, not bolted on
DataOps, Quality & Governance
- Automated data quality checks and end-to-end observability
- Schema drift caught before it reaches a dashboard
- Cataloging, lineage, and access control that stand up to audit
AI & Analytics Enablement
- RAG-ready semantic layers and metadata for GenAI workloads
- Real-time data feeding the dashboards and models teams rely on
- Clean, documented foundations your AI retrieves instead of hallucinating
Why
No two data stacks break the same way, so we don't ship a reference architecture and walk away. Every engagement starts with your business questions, your team, and your stage. Then we design the platform that fits.
A seed-stage startup drowning in spreadsheets doesn't need what a mid-market SaaS with compliance exposure needs. We size the tooling, the process, and the spend to where you actually are.
That's why everything starts with the audit: a blueprint fitted to your stack, not a template fitted to ours.
We name our dbt models for fun, so trust us, nobody enjoys bringing order to a messy data estate more than we do. Five script folders, three 'final_v2' dashboards, one mystery cron job: we can't wait to get our hands in there.
Sources need to be sifted through. Models need naming conventions. Pipelines need owners, tests, and a place to live in version control.
Our data-as-code process comes with catalogs, lineage, and documentation included, and it removes the friction so your team always knows where everything is and why.
Pipelines that work in the demo and fall over at ten times the volume are a countdown, not infrastructure. We build for the data you'll have, not just the data you have today.
Infrastructure-as-code, CI/CD, and automated testing mean growth doesn't break things: new sources, new models, and new teammates slot into a system that already knows how to absorb change.
And scale includes the bill. We design warehouses and pipelines that grow without your cloud spend growing faster.
We've spent 20+ combined years building data infrastructure at Big Tech. Blazing forward without a plan never ends well, so we think first, then build.
Our process moves step by step from audit to build to operate, with checkpoints at every phase, so you always know where the work stands and nothing gets lost in a handoff.
When roadblocks come, we don't cut corners. We get creative, whether that means a phased rollout, a different tool, or a simpler design. The engineering standard stays the same no matter what.
Process
Audit
A build-ready blueprint that names what's broken, what it costs, and what to fix first.
- Stakeholder interviews
- Pipeline & architecture review
- Data quality & cost profiling
Build
Production pipelines that hold: versioned, tested, and deployed with Big Tech discipline.
- Version control & code review
- Automated testing & CI/CD
- Infrastructure as code
Operate
A platform your team runs independently, with senior judgment on call.
- Observability & alerting
- Documentation & handoff sessions
- 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.