In 2026 Moberg became a Databricks Consulting & Solution Implementation Partner — the validation tier for firms that don't just advise on the platform but deliver and operate it. For us it formalized something that had been true for years: Databricks is where our largest data platforms live.
The platform we'd already bet production on
Alongside the Microsoft designations, it completes a deliberate position: certified depth on both sides of the modern data-platform choice, so our recommendation is driven by your case — not by the only badge we hold.
Earned, not granted
Databricks validates its implementation partners on certified people and delivered work. The prerequisites are the achievement. Behind this badge:
- Certified people — Professional-grade engineers
- Databricks Certified Data Engineer Professional and Associate credentials, plus Platform Architect accreditation across the team — maintained, not collected once.
- Delivered platforms — Lakehouses in production
- A 10 TB Medallion lakehouse consolidating an investment-banking group — ROI 80%+ in year one — and multi-business data lakes across grocery, e-commerce and pharmacy.
- Operating record — Run, not just launched
- Unity Catalog governance, Asset Bundles CI/CD and production ML with full lineage — platforms operated month after month, with the audit trail to show it.
What this means for our clients and partners
The practical difference is in the advice, not the logo. Holding certified depth on both Databricks and the Microsoft data stack means the platform recommendation you get is argued from your workloads, your existing estate and the team who will run it afterwards — rather than from the one platform we happen to be accredited on.
It also means the reference architectures we propose are ones we operate. Medallion on Delta Lake, Unity Catalog governance, Asset Bundles CI/CD and production ML with lineage are not slideware here; they are how the platforms behind this badge were built and are still run. That shortens the distance between a design review and a working environment, and it means the trade-offs come with the reasons attached.
And because the tier is validated on delivered work rather than on training alone, it is a statement about what has already shipped. If you are weighing Databricks against another platform, or you already run it and want it operated properly, that is the conversation we are set up to have.
