Whole-system thinking
I naturally look across the application, database, integration, data platform, reporting and AI layers instead of optimizing one component in isolation.
About · Edmonton, Canada
I work across the boundaries where applications, data and people meet.
My career started in database applications and grew through full-stack software, client delivery, technical leadership, e-commerce systems, analytics and now Data & AI solution architecture.
Today I design business applications, Microsoft Fabric data platforms and AI-enabled workflows. The common thread is not a particular technology — it is understanding the whole system well enough to make the parts work together.
I stay close to implementation because architecture is stronger when it has to survive contact with real users, real data and production constraints.
20+ yearssoftware, business systems, analytics and data
End-to-endrequirements, architecture, implementation and handover
1,000+people trained across 12 countries
Current focusMicrosoft Fabric, Data & AI and application architecture
What I bring
A solution architect who still understands what has to be built.
My strongest work sits between business requirements and technical delivery — where someone has to understand the process, make the architectural decisions, and stay close enough to implementation to know whether those decisions hold up.
Hands-on architecture
I can move from solution design into Python, SQL, PySpark, Fabric pipelines, data models, APIs or application code when the architecture needs to be tested against reality.
Business-to-technical translation
Years of client work, project leadership and business systems taught me to translate what people ask for into the problem that actually needs to be solved.
Knowledge transfer
Documentation, runbooks, explanation and training are part of delivery. A system should remain understandable and operable after the original builder steps away.
Career progression
The path has been wide. That breadth is useful now.
I did not arrive at solution architecture through one narrow technical track. Each stage added a different view of how systems are built, used, managed and explained.
2006–2010
Database applications → full-stack web
Started with Oracle database applications, then moved into custom web development and end-to-end client projects. This is where I learned the discipline of understanding the requirement before writing the code.
2010–2015
Senior development → technical leadership
Built and optimized web applications, managed parallel client work and later led development projects with responsibility for scoping, technical direction and delivery.
2015–2020
Business systems, e-commerce and analytics
Moved beyond software delivery into operational systems, team leadership, automation, growth and KPI-driven decision making. Technology became inseparable from the business outcome.
2021–2024
Independent BI consulting and teaching
Delivered reporting and analytics work while teaching more than 1,000 practitioners across 12 countries. Working with mostly non-technical audiences sharpened how I explain data, trade-offs and decisions.
2024–2025
Canada: application and data consulting
Continued independent work in Edmonton while deliberately shifting my technical centre of gravity toward the Microsoft data platform and modern application architecture.
2025–Now
Data & AI solution architecture
Designing and building business applications, Microsoft Fabric medallion platforms, Power BI reporting and AI-enabled workflows with human review — the application, the platform beneath it and the AI layer on top.
How I think about architecture
Good architecture reduces friction between layers.
The diagrams matter, but the real test is whether the system remains understandable, supportable and useful when it reaches production.
Start with the workflow, not the product.
Users, process, constraints, data ownership and expected outcomes come before choosing Fabric, Power Apps, Django or any other technology.
Data has a life before and after the lakehouse.
Operational systems create it; the platform governs and models it; analytics explains it; AI can bring it back into the workflow. Architecture should account for the complete path.
Handover is part of the design.
Documentation, runbooks, training and clear operational ownership are not cleanup tasks. They are part of what makes a system production-ready.
Current focus
Where I am deliberately going deeper now.
My background is broad, but the next stage is intentionally concentrated around the Microsoft Data & AI ecosystem and client-facing solution architecture.
01 · Data platform
Microsoft Fabric
Building depth across engineering, modeling, governance, performance and lifecycle practices for enterprise data platforms.
- Lakehouse and medallion architecture
- PySpark and SQL engineering
- Semantic models and Power BI
02 · Solution design
Application + data architecture
Connecting operational systems to the analytical platform rather than treating applications and data as separate disciplines.
- Python/Django and React
- PostgreSQL, Power Apps and Dataverse
- Integration and data ownership
03 · AI
AI inside business workflows
Designing AI around controlled assistance, useful context and human review rather than adding a disconnected chatbot to an existing process.
- Azure AI services
- Document intelligence and retrieval
- PII protection and human review
Communication & teaching
Technical depth only matters if people can act on it.
I have taught live cohorts, worked with non-technical audiences and led client-facing delivery. That experience now shows up in discovery conversations, architecture explanations, documentation, training and handover.
Teaching & knowledge transfer →1,000+practitioners trained
12countries represented
Connect
Architecture, Microsoft Fabric, data platforms and practical AI.
I am based in Edmonton and interested in conversations about enterprise Data & AI architecture, Microsoft Fabric and the systems behind real business workflows.