Bridging business needs and technology with clear requirements and reliable data.

Challenge

• HR candidate data from Lever was split across six Excel files
• Resumes were held in a separate bulk export
• Scope changed shortly before the deadline

My Role

• Performed the data migration end to end
• Rebuilt the Lever data structure from scratch

Approach

• Analyzed the data to design an efficient, accurate structure
• Defined primary and foreign keys to prevent discrepancies
• Built linked datasets and data models in Power BI, SharePoint, PowerShell, and Excel
• Matched each resume to its candidate profile ID

Result

• 450,000 records restructured
• Six Excel files combined into one structured file
• Delivered on schedule despite late scope changes
• HR can open any of 160,000 filtered profiles, with its resume

Challenge

• Risk of losing skilled local talent to other provinces and employers
• No structured way to support eligible employees building careers in Ontario

My Role

• Identified the opportunity and built the business proposal
• Led discussions with HR Business Partners, EVPs, and the Directors of Technology and HR

Approach

• Researched the Ontario Immigrant Nominee Program (OINP), an active Government of Ontario stream
• Framed employer support for eligible employees as a retention strategy
• Worked with the EVP of HR to initiate the process

Result

• Proposal taken forward by the organization
• Eligible employees gained access to employer-supported OINP pathways

Challenge

• Transit operators lacked real-time insight into bus condition
• Needed support for proactive maintenance, efficiency, and passenger experience

My Role

• Project Manager and Associate Developer in a Scrum team
• Led requirements, planning, design, development, testing, and deployment

Approach

• Translated business and user requirements into hardware and software requirements
• Ran sprint planning, stand-ups, and code reviews
• Designed an IoT architecture with real-time sensors and threshold-based alerts feeding an Android app

Result

• Delivered from concept through deployment
• Live sensor dashboards, alerts, offline-first sync, and Firebase authentication
• Verified with automated tests in JUnit, Espresso, and Robolectric

Challenge

• Phone pickup orders and repeat questions pull staff away from dine-in guests
• Missed calls and kitchen stock checks cause delays and wrong orders
• Uber Eats pickup costs the restaurant commission and customers higher prices

My Role

• Scoped a WhatsApp ordering line for a fictional restaurant as an AI Builder project
• Wrote seven business rules and built the n8n workflow end to end
• Debugged and documented it across several test rounds

Approach

• Moved pickup ordering to a WhatsApp chat
• Kept stock, FAQs, and orders in Google Sheets for live lookups
• A LangChain agent on Google Gemini handles the conversation; the workflow generates order IDs

Result

• Six test orders logged, including a 21-item, $274.35 order
• Order details are emailed straight to the restaurant, and stock updates automatically
• Customers get a pickup time based on order size, and pay menu price with no Uber Eats commission