| View Code Review | Enhancement One: Software Design and Engineering | Enhancement Two: Algorithms and Data Structure | Enhancement Three: Databases |
Completing the Computer Science program at Southern New Hampshire University has been a unique experience for me because I entered it from a different direction than most students. By the time I finished my degree, I had already been working professionally as a software engineer, building factory automation systems at John Deere, consulting on AI integrations for clients across oil and gas, medical supply chain, and SaaS industries, and deploying AI agents at Occidental Petroleum and Microsoft. The program did not launch my career. My career was already underway. What the program did was give me the formal foundation to understand why the things I was doing worked, fill in gaps I had developed by learning on the job, and push me to think more deliberately about software engineering principles that I sometimes applied instinctively without fully articulating them. Developing this ePortfolio brought those experiences together in a way that clarified my strengths and sharpened how I communicate them to others.
The program consistently emphasized collaborative development through agile methodologies, iterative workflows, and structured team communication. While these frameworks were already familiar to me professionally, working through them academically helped me articulate them more precisely. At John Deere, I oversaw the full SDLC for twelve application rollouts, coordinating tasks, timelines, and stakeholder communication across factory departments. At Occidental Petroleum and Microsoft, I operated within agile cycles alongside cross-functional teams to deliver AI features on tight schedules. My volunteer work with Code Nation, where I mentored over fifteen students weekly and supported two student-led hackathons through coaching and code reviews, further developed my ability to collaborate across experience levels and adapt my communication style to the person in front of me. The program reinforced the formal vocabulary behind these practices and gave me a clearer way to describe what I was already doing.
One of the most consistent demands of my professional work has been translating complex technical decisions into language that non-technical stakeholders can act on. At Capital Group, I design and lead user workshops and enablement sessions to drive adoption of AI-powered tools across enterprise teams. At Microsoft, I collaborate with writers, producers, analysts, and marketers to identify automation opportunities and build scalable agentic workflows. The program reinforced this skill through assignments that required documented design decisions, structured written reflections, and formal project documentation. An example from outside my capstone artifact is the software design document I produced for The Gaming Room project, where I evaluated platform architectures, developed a domain model, and delivered a clear technical recommendation to a client audience. That project sharpened my ability to present trade-off analysis in writing in a way that is technically sound but accessible to a non-engineering reader.
The program gave me a formal framework for analyzing algorithmic decisions that I had previously made largely by experience. A clear example from outside my capstone artifact is the Course Advising System I built, where I evaluated three data structure options, vector, hash table, and binary search tree, and selected a BST based on its natural sorting behavior and efficient access patterns for the use case. That kind of structured trade-off analysis, weighing the properties of different data structures against specific problem requirements, is exactly the type of thinking that carries into professional engineering decisions. In my professional work, similar reasoning has informed how I structure data pipelines, organize agent knowledge sources, and design retrieval systems for AI applications. The program gave me the language and the framework to make those decisions more deliberately and defend them more clearly.
The program reinforced software engineering fundamentals that I apply daily in my professional work, including test-driven development, input validation, modular design, and secure coding practices. Outside of my capstone artifact, my software testing portfolio demonstrated this through the ContactService project, where I built unit tests that enforced field constraints such as exact ten-digit phone number validation and non-null field requirements, ensuring that every test mapped directly to a user-facing requirement. This test-driven mindset, designing software to be verifiable and not just functional, is one I carry into every project. At John Deere, it informed how I built and validated factory dashboards handling live production data. At Capital Group and Microsoft, it shapes how I design and document AI agents to ensure they behave reliably and are maintainable by teams beyond my own.
On the database side, the program deepened my understanding of relational data modeling, schema integrity, and the relationship between database design and application maintainability, skills I applied directly in my capstone and that inform how I approach data architecture decisions professionally.
Security has been a recurring theme throughout both my academic work and my professional career. In the program, the Artemis Financial security enhancement project was a formative experience outside of my capstone. For that project, I implemented SHA-256 checksum verification, enabled HTTPS communication using Java Keytool, and conducted vulnerability analysis using OWASP Dependency-Check to identify risks in third-party dependencies. That project made the connection between secure coding practices and real client data protection concrete and immediate rather than theoretical.
Professionally, security considerations are embedded in nearly everything I do. At Capital Group, I collaborate with InfoSec stakeholders to establish DLP policies, governance standards, and secure AI deployment practices. At Occidental Petroleum, I fine-tuned LLM models in Azure AI Foundry to minimize hallucinations and safeguard sensitive proprietary information, reducing user escalations by 35% and improving search speed and accuracy by 75%. The program reinforced that security is not a feature added at the end of a project. It is a discipline that must be built into every layer of a system from the beginning.
The three artifacts and enhancements that follow this self-assessment together tell a complete story about what it means to improve a real application holistically across all layers of its architecture.
The first artifact, the Software Design and Engineering enhancement, addresses the view layer of RateMyLandlord. It demonstrates my ability to evaluate an existing codebase, identify maintainability and usability problems, and apply consistent software engineering practices to produce a cleaner, more extensible application without breaking existing functionality.
The second artifact, the Algorithms and Data Structure enhancement, addresses the search system. It demonstrates my ability to design and implement a multi-token normalization pipeline, a progressive AND-join filtering algorithm, and a field-weighted relevance ranking system, making deliberate trade-off decisions at each step and ensuring the implementation is both algorithmically sound and secure against injection vulnerabilities.
The third artifact, the Database enhancement, addresses the foundation of the application. It demonstrates my understanding of relational database design, schema integrity, indexing strategy, foreign key enforcement, and the downstream impact that database changes have on every other layer of the application.
Taken together, these three enhancements demonstrate that I approach software not as a collection of isolated features but as an integrated system where design, logic, and data must all be considered simultaneously. That perspective, shaped by both professional experience and the formal rigor of this program, is what I bring to every engineering problem I work on, and it is what this portfolio is designed to show.
| View Code Review | Enhancement One: Software Design and Engineering | Enhancement Two: Algorithms and Data Structure | Enhancement Three: Databases |