Project 1: AI-Assisted Web Privacy & Risk Automation
The Challenge: Assess enterprise web compliance and security vulnerabilities across a large, distributed global web footprint.
The Action: Built a custom data pipeline using Python and Google Colab to systematically parse and analyze 613 HAR files across 56 separate brand domains.
The Outcome: Identified 156 critical-risk and 342 high-risk pages, delivering the precise data architecture required to steer portfolio-wide enterprise remediation.
Project 2: Terex B2B Dealer Portal Modernization
The Challenge: Overhaul legacy dealer systems to improve global B2B digital operations and increase active engagement across 1,000+ dealers.
The Action: Led cross-functional engineering teams using RICE-prioritized product backlogs backed by data insights from GA4, GTM, and BigQuery. Compressed Agile sprint cycles from 3 weeks to 2 weeks to maximize feedback loops.
The Outcome: Scaled platform adoption by 400%, growing active portal users from 5,000 to over 20,000+.
Project 3: M&A Digital Governance & Platform Consolidation
The Challenge: Manage the technical integration and alignment of 15 legacy web properties following a major enterprise merger, ensuring zero downtime and strict brand alignment.
The Action: Established uniform platform governance, consolidated disparate codebases, and coordinated cross-functional internal engineering teams alongside vendor contributors.
The Outcome: Successfully integrated all 15 marketing and dealer sites into the centralized digital portfolio pipeline while optimizing long-term platform delivery.
Project 4: E-Commerce Catalog Automation & Multi-Tenant Data Integrity
The Challenge: Following a critical B2C website split into two distinct brand domains (://genielift.com and myparts.terex.com), an existing 22,000-product Proof of Concept (PoC) in Google Merchant Center required rapid data segregation. Overlapping Manufacturer Part Numbers (MPNs) created a high risk of cross-tenant data corruption and unverified product listings on Oracle CX.
The Action: Leveraged knowledge from graduate studies at the Gies College of Business (UIUC) to design a local Python script running in VS Code. Implemented an AI-assisted search algorithm to query the live Oracle CX e-commerce environment, dynamically parsing ambiguous MPNs based on site presence. Systematically isolated edge-case data errors to produce perfectly clean data sets.
The Outcome: Successfully populated separate B2C subaccounts in Google Merchant Center, activating Google Shopping visibility to drive B2C sales volume and scale the catalog PoC. Currently leveraging this data-validation success to explore how teams can scale QA efficiency using frameworks like Python & Playwright.