Databricks explained to the people who write and run the code.
Sessions for data, platform, and app-dev teams that need to build and operate — not sit through a slide catalog. Grounded in your stack, your repos, and your governance constraints.
TWO FORMATS
Architecture session
Data / platform / app-dev teams
Half-day or full day: reference architecture, metadata-driven patterns, and what the team can implement next.
From
$2,500
Message on LinkedInEnablement program
Your people, your tools, your use case
We start from their environment and a real pipeline or service, then build sessions so they leave able to ship.
Quoted per engagement · depends on format
Let's scope the programMY METHOD
Start from code and stack, not PowerPoint.
Stack, DataOps maturity, SOC/compliance constraints, and what the team must deliver.
A pipeline, a catalog, an API, a Helm chart — something they already touch.
Exercises on their patterns: metadata, quality, orchestration, app surfaces.
Conventions, checklists, and next implementations the team can hold on its own.
Delivered solo or with your internal tech leads — whichever embeds the practice best.
WHAT I TEACH
Topics for engineers who operate the platform.
Reference architecture
The building blocks of a data/AI/app platform and how to assemble them without over-engineering.
Catalog & metadata
Make assets findable, governed, and consumable via APIs.
Metadata-driven ingestion
Pipelines that adapt to schemas — not every manual ticket.
DataOps & quality
Automated controls, profiling, and integrity in data CI/CD.
Secure consumption
Environments (e.g. Databricks + controls) and access patterns ready for audit.
Apps on the platform
Microservices, APIs, and consumption surfaces that respect governance.
WHY ME
8+ yrs
building data, cloud, app, 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, orchestration, APIs, and platform architecture.
IN THE FIELD
What teams take with them.
A shared engineering practice.
Transfer around catalog, metadata-driven pipelines, and quality — with Databricks and Kubernetes.
Ops
the team prioritizes and operates without waiting on the next external.
A shared architecture language.
Align data, platform, and app-dev on the same patterns — so the build holds.
Align
less friction between design and delivery.
A team to level up on the stack?
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