My path to analytics wasn't traditional, but it's been intentional.

I started in education and social work, fields that sharpened my ability to understand complex systems, work with messy data, and communicate clearly to people who need answers, not spreadsheets. Over time I realized the work I found most energizing was the analytical layer underneath it all: tracking outcomes, identifying patterns, and building tools that made hard decisions easier.

So I pursued that directly.

Today I work as a Data Analyst at Dynatrace, where I build Python pipelines that merge multiple data sources, score accounts by renewal risk, and deliver personalized reports to sales teams; work that went from a manual hours-long process to running in under 30 seconds. I'm also building an AI layer that adds subjective signal to our churn model, pulling insight from unstructured Salesforce notes that the existing model couldn't see. It's still early, but it's already shaping how we think about renewal risk beyond the numbers. 

Before that, I engineered a constraint-based scheduling system for Trinity Health that automated 6-month physician schedules across five hospitals which replaced a 3-month manual process. The system handled 550+ unique provider preferences, FTE-weighted contract targets, credentialing rules, rest buffer logic, and then outputted a production-ready import file.

I work across Python, SQL, Power BI, and Excel, and I'm particularly drawn to problems where the data exists but no one has built the right system around it yet.