Core Capabilities
What I bring to a BI or analytics engineering team.
Microsoft Power Platform
Extending Business Intelligence into apps, workflow, and governed data.
The Microsoft Power Platform extends Business Intelligence and Analytics Engineering past reporting — into the workflows, applications, and governed data that decisions actually run on. Power BI is where I have real reporting depth today; Power Automate, Power Apps, and Dataverse are the direction I'm deliberately building toward next.
Power BI
Current capability- Executive dashboards
- KPI reporting
- Semantic models
- Data visualization
- DAX
- Power Query
Power Automate
Developing capability- Workflow automation
- Approval routing
- Notifications
- Scheduled processes
Power Apps
Developing capability- Internal operational applications
- Guided review workflows
- Business process digitization
Microsoft Dataverse
Developing capability- Business entities
- Data governance
- Business rules
- Relationships
- Audit history
Exploring — not production experience
Power Pages and Copilot Studio are technologies I'm currently exploring. I'm naming them here for transparency about direction, not claiming hands-on production work with either.
Current capability
Developing capability
Analytics Engineering — Developing Specialization
Not current professional experience — a specialization I'm building on my BI foundation.
A developing specialization, built directly on top of the BI foundation above — not a separate track.
Data Modeling
Kimball dimensional modeling applied to the ESIP star schema.
ETL Concepts
Power Query and governed ETL pipelines moving raw data into clean, validated tables.
Validation Frameworks
Rules applied at generation/ingestion time rather than discovered downstream.
Semantic Models
Reusable DAX measure libraries defining each KPI once, referenced consistently across reports.
Enterprise Reporting
Executive-facing dashboards built on governed, documented data models.
Testing
Automated test coverage on ETL logic (43/43 passing on the reporting-automation pipeline).
Documentation
Data dictionaries, relationship diagrams, and architecture decision records as standard practice.
Reusable Architectures
Configuration-driven, seed-driven systems designed to be reconfigured, not rewritten.
AI-Assisted Solution Development
AI accelerates the work. I own every decision.
AI as an accelerant for solution design — not a replacement for engineering judgment, and never auto-publishing without review.
AI accelerates my work, but every business decision, architecture choice, validation, testing, and final approval remains under my ownership.
Business analysis & requirements gathering
AI accelerates surfacing and structuring requirements — I own what actually goes into the scope.
Architecture exploration
Used to compare design options quickly, as with the three architecture paths evaluated for the PIM platform — the recommendation and decision are mine.
Technical documentation
Architecture decisions, risk registers, and open questions tracked explicitly before code is written.
Prototype development & code assistance
Used throughout this site's own development, documented rather than hidden.
Testing support
Validation rules enforced at generation/ETL time, with AI helping generate test coverage — not replace it.
Solution refinement
Iterating on a working design faster, with every refinement reviewed before it ships.
Python automation
Data generation, validation, and analytics pipelines across ESIP and the Commercial Analytics Dashboard.
Human-in-the-loop review
Every AI-assisted design here — the PIM platform especially — keeps a human approval step by design.
Explainable business rules
Validation and governance logic that can be read and audited, not opaque model output.