Databricks explained to the people who actually run the platform.
Training for data, platform, and product teams that need to build and operate — not just sit through slides. Grounded in your stack, your pipelines, and your governance constraints.
TWO WAYS TO START
Intro workshop
Data / platform teams · intermediate
Half-day or full day: reference architecture, metadata-driven design, and what participants can prioritize by the end.
From
$2,500
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Your people, your tools, your use case
We start from their environment and a real use case, then build sessions so the team leaves able to operate.
Quoted per engagement · depends on format
Let's scope your programMY METHOD
Start from the field, not a slide catalog.
Stack, maturity, SOC/compliance constraints, and what the team must deliver.
A pipeline, a catalog, a portal — something they already touch.
Exercises on their patterns: metadata, quality, orchestration, consumption.
Checklists, conventions, and next steps the team can hold on its own.
Delivered solo or with your internal partners — whichever embeds the practice best.
WHAT I TEACH
Topics for teams that operate the platform.
Reference architecture
The building blocks of a data/AI platform and how to assemble them without over-engineering.
Data catalog
Make assets findable, governed, and useful to the business.
Metadata-driven ingestion
Pipelines that adapt to schemas — not every manual ticket.
Quality & profiling
Automated controls for integrity and compliance.
Secure consumption
Consumption environments (e.g. Databricks + controls) ready for audit.
Platform leadership
Prioritize the roadmap, raise the practice, and align product + engineering.
WHY ME
8+ yrs
building data, cloud, and AI platforms in enterprise context.
SOC2
secure consumption and compliance experience.
K8s
real pipeline orchestration — not just theory.
The common thread: I teach what I've shipped — catalog, metadata-driven design, governance, and technical leadership.
IN THE FIELD
The result teams take with them.
A shared platform practice.
Transfer around catalog, metadata-driven pipelines, and quality — with Databricks and Kubernetes in the background.
Ops
the team can prioritize and operate without waiting on the next consultant.
A shared architecture language.
Align product, data, and platform on the same patterns — so the roadmap holds.
Align
less friction between intent and delivery.
A team to level up on platforms?
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