Work from my internship at JLL and coursework at MSU Mankato.
JLL Retail Corporate Services · Fall 2024
A six-dashboard Tableau workbook, connected live to JLL's Oneview PostgreSQL database, tracking hundreds of franchise sites through site selection, lease negotiation, and build-out. Stage-duration stoplights, a radius-based site map, and lease comps, with LOD calculations behind the benchmarks.
JLL's team tracks commercial net lease properties across Texas for market intelligence — clients want to know what cap rates look like, how many listings are hitting the market each quarter, and what the investment grade mix is. This was a two-page Tableau dashboard covering Houston specifically and all of Texas statewide.
Each page shows cap rate trends in both bar and line form so you can see the shape of the market over time, plus remaining lease term distribution, investment grade vs. non-investment grade breakdown, and a QoQ change indicator with a directional arrow that flips color based on whether volume went up or down. I wrote the inclusion logic to filter to prior-year listings and built the percentage change calculations from scratch since Tableau doesn't do that automatically across time periods without table calculations.
Built alongside the Tableau dashboards as a Power BI version for a client pitch, tracking store and site development the same way JLL's team does day-to-day — by format, market, and current phase — for teams that run on Power BI instead of Tableau.
The report has a summary page rolling up site counts by format, market, and phase, plus a single-site drill-down view with map and site-name tooltips so a client could click into any individual location during the pitch and see exactly where it stood in development.
Client name, contact names, and the specific site location are blacked out below since this was real client work, not a synthetic dataset — the layout and functionality are unchanged.
One of the more unusual things I built during the internship. Franchisees evaluating commercial real estate need to compare the long-term cost of leasing space against buying it outright, and that math gets complicated fast once you factor in vacancy, depreciation, loan amortization, and capital gains on a future sale.
I built a reusable Excel model with two versions — one that accounts for vacancy and one that doesn't, since not all properties have that exposure. You plug in purchase price, loan terms, rent escalation rate, expense growth, and a discount rate, and it runs a 10-year cash flow projection for both scenarios side by side with NPV and IRR outputs. The goal was something a franchisee could actually use themselves, so I kept the inputs clearly labeled and the outputs in plain language.
Coursework · MSU Mankato · Spring 2026
A two-page Power BI report built from a raw supply chain CSV: data audit and transformation in Power Query, a star schema around FACT_ORDER_DETAILS, and DAX measures for revenue and on-time delivery.
Systems analysis and design for a system that automates disruption recovery when a Disney ride goes down: five processes, a full set of DFDs, a five-entity ERD, an Access prototype, and a 20-week plan. I led the team and was the main contributor.
A 12-table MySQL registration system with capacity-enforcing triggers, stored procedures, functions, views, and five access roles. I handled the schema design, triggers, stored procedures, and role-based access control.