About

Why we are here

My goal is to share news, experience, and guidance — mixed with brief personal opinion and the occasional story. I hope to share and learn from the community.

I always welcome appropriate feedback. Learning from the community and sharing in return is the ultimate purpose for the content I provide. Lately, that includes a working lab — small but real projects (a lakehouse for our local golf league, an agent‑powered web app, a finance reconciliation app wired to Databricks) — that I use to put modern data and AI patterns under load and write about what actually works.

About Me

My career has been rooted in communications networks, infrastructure integration, and complex systems engineering — long before AI became a mainstream enterprise conversation. I began in military communications, serving as an Air Force communications specialist and later as a systems administrator in the U.S. Navy. Those early roles established a deep technical foundation in reliability, security, and mission‑critical operations — principles that continue to shape how I approach modern data and AI solutions today.

In the late 1990s and early 2000s, during the deregulation of the telecommunications industry, I transitioned into the competitive local exchange carrier (CLEC) space. This period exposed me to large‑scale carrier environments, operational engineering, and the realities of designing systems that must scale, interoperate, and perform under real‑world constraints. I worked across enterprise and carrier platforms, focusing on SIP routing, session border controllers, commercial call routing, virtualization, automation, and carrier integration.

At Comcast, I partnered closely with engineering and operations teams on carrier‑grade SIP routing solutions, scalability initiatives, and automation strategies — helping bridge the gap between traditional telecom infrastructure and emerging IP‑based communication platforms. Later, as an Enterprise Infrastructure Director for a high‑growth electronics manufacturing company, I aligned infrastructure strategy with manufacturing operations and business growth, reinforcing the importance of technology as an enabler of outcomes — not an end in itself.

Microsoft — From Unified Communications to Enterprise AI

I joined Microsoft’s Customer Success organization as a Converged Communications Cloud Solution Architect (CSA), bringing my carrier and enterprise infrastructure background into the world of Unified Communications and UCaaS. My work centered on Microsoft Teams Voice, enterprise collaboration, and large‑scale voice deployments for commercial and manufacturing enterprises. I maintained certifications as a Teams Voice Engineering Expert and Collaboration Communications Engineer, supporting customers through complex migrations, voice architecture design, and end‑user adoption.

As collaboration platforms matured, customer conversations shifted. Organizations weren’t just asking how to enable voice or meetings — they wanted to understand how technology could augment work, accelerate decision‑making, and automate business processes. That shift mirrored my own. I transitioned into an AI Business Solutions CSA, where my background in communications, infrastructure, and system integration naturally extended into AI agents, intelligent automation, and enterprise AI architecture.

In that role, I focused on helping customers design and deploy AI agents that went beyond Q&A — agents grounded in enterprise data, orchestrated across systems, and operationalized at scale. I worked extensively with Copilot Studio, Azure AI, and agent orchestration patterns, helping organizations build goal‑driven, action‑oriented AI solutions that integrate with business processes, data platforms, and applications. A specialty became bridging low‑code and pro‑code — pairing the rapid value of Power Platform and Copilot Studio with the depth and durability of Azure, custom APIs, and pro‑code agents as requirements matured.

Databricks — Field Engineering Leadership, Manufacturing & Automotive

I’ve now joined Databricks in a Field Engineering Leadership role focused on the Manufacturing and Automotive verticals — a direct continuation of the impact I built with industrial and automotive customers during my time at Microsoft. The shift is less about changing direction and more about going deeper: from helping customers adopt AI on top of business platforms, to helping them build the data foundation that makes durable, governed AI possible in the first place.

My focus areas in this role:

Data and AI at industrial scale — lakehouse architectures, streaming, and ML/AI workloads that meet manufacturing’s operational realities (latency, traceability, OT/IT integration, plant‑floor pragmatism).
Agent development — moving from Q&A bots to multi‑step, tool‑using agents grounded in trusted enterprise data. The same patterns I used in Copilot Studio and Azure AI now extend into Mosaic AI Agent Bricks, Genie, and the Databricks Intelligence Platform.
Architecture and governance — Unity Catalog–centric designs that put lineage, permissions, audit, and cost at the center, not as afterthoughts. Industrial customers can’t deploy AI they can’t explain or govern, and I help teams design accordingly.
Fusion teams and cross‑disciplinary delivery — connecting data engineers, ML/AI engineers, low‑code builders, and business stakeholders so solutions don’t stall at platform or team boundaries.

How that shapes what I write here

The projects I share on this blog aren’t hypotheticals. Recent work includes:

A full Databricks lakehouse built around a real‑world data source (my local golf league), exercising bronze/silver/gold medallion design, Lakeflow Declarative Pipelines, Unity Catalog governance, Genie spaces, and a Mosaic AI agent that reasons over the data.
A keyless, governed integration between an Azure web app and Databricks using a Managed Identity + Unity Catalog grants — a small but realistic blueprint for enterprise app↔lakehouse connectivity without secret sprawl.
Structured data analysis writeups — turning messy operational data into trustworthy gold tables and the kind of analytics business owners can actually act on.

The throughline across two decades of work — military comms → carrier voice → enterprise infrastructure → Microsoft AI → Databricks data + AI leadership — is the same: designing systems that work in the real world. Systems that scale, integrate cleanly, respect governance, and produce measurable business outcomes.

That’s the lens I bring to everything I share here. If something resonates, breaks, or sparks a better idea — I’d love to hear it.