Analytics grounded in how the business actually works.
My career started inside a distribution center and progressed through operational leadership, quality performance, operations analytics, and senior data roles. That path shaped how I analyze problems today: I want to understand both what the data says and the real process behind it.
Start with the problem, not the dashboard.
I am most effective when a question is messy, the data is incomplete or difficult to interpret, and the answer requires both technical investigation and business context.
I have built SQL and BI solutions, automated ETL and reporting workflows, normalized unstructured error logs, supported data-lake modernization, developed business cases, and worked across technical and operational teams to validate findings before turning them into action.
My professional use of AI follows the same principle. I use tools such as Microsoft Copilot and Databricks Genie Code to accelerate research, synthesis, and SQL development, while treating the underlying data and business logic as the source of truth.
Clarity, evidence, and useful outcomes.
I care about analytical work that can be explained, defended, and used. A technically impressive solution is only valuable if the people who rely on it understand what it means and trust the logic behind it.