Turning complex data into reporting people can use.
I build analytics and reporting solutions that translate complex business rules into clear, reliable tools for decision-making. My strongest domain experience is in nonprofit and fundraising analytics, with methods that also transfer to operational and corporate environments.
Analytics that has to work for the person using it.
These portfolio concepts use synthetic data and a fictional Chicago nonprofit. They demonstrate information architecture, business-rule translation, analytical thinking, and Power BI visual development without exposing employer or constituent information.
What I work with.
The focus is not simply producing a dashboard. It is connecting data structure, business definitions, validation, and visual design so the finished output is understandable and reusable.
Business Intelligence
Power BI, DAX, Power Query, dashboard architecture, paginated reporting, KPI design, report navigation, and stakeholder-ready visual layouts.
Modeling & Transformation
Data relationships, reusable calculations, business-rule modeling, Power Query transformations, and Python-based data preparation.
Nonprofit Analytics
Donor segmentation, retention and acquisition, campaign analytics, giving behavior, RFM, prospecting, portfolios, grants, and opportunity pipelines.
Reconciliation & Validation
Source-to-report validation, CRM reconciliation, exception reporting, business-rule testing, reporting definitions, and data-quality monitoring.
Repeatable Workflows
Power Automate, file and notification workflows, recurring reporting processes, documentation, and reducing unnecessary manual handling.
Python & Forecasting
Python transformation, analytical exploration, predictive analytics, time-series forecasting with Prophet, trend analysis, and prototyping.
Native first. Custom when it earns its place.
I use native Power BI functionality when it communicates the requirement clearly. When a reporting problem needs denser layouts or more specific visual logic, I can build custom HTML/CSS-style components driven by report context and calculations.
Native Power BI
Use the standard visual layer when it gives users the clearest, most maintainable reporting experience.
- KPI cards and standard charts
- Tables and matrices
- Slicers and drill-through
- Conditional formatting
- Reference lines and goal tracking
- Report navigation and bookmarks
Custom visual treatment
Extend the presentation when native visuals cannot express the required layout, density, or business logic cleanly.
- Goal + stretch-goal progress displays
- Portfolio coverage indicators
- Dynamic progress and benchmark markers
- Conditional status components
- Dense operational reporting layouts
- Context-aware KPI components
Transformation, exploration, and forecasting beyond the report layer.
Python complements my BI work when the analytical problem benefits from more flexible transformations, repeatable processing, predictive analysis, or prototyping.
Recent work includes Prophet forecasting
I use Python for data transformation, analytical exploration, repeatable processing, predictive analytics, and time-series forecasting. Prophet has been part of my recent forecasting work for modeling historical patterns and producing forward-looking projections.
From business question to reliable reporting.
The visual is only one stage of the work. I focus on the definitions, transformations, calculations, validation, and repeatability behind it.
Analytics with the business context included.
My strongest domain experience is in nonprofit and fundraising analytics: donor behavior, segmentation, campaigns, relationship management, portfolio reporting, operational reporting, reconciliation, and forecasting.
The same analytical methods can transfer to corporate, finance, operations, customer, and sales environments. The goal remains the same: make the definitions understandable, make the numbers trustworthy, and make the output useful.
