These projects represent my commitment to continuous learning and hands-on technical development. Each demonstrates practical application of enterprise data management concepts, modern technology stacks, and my ability to learn by building.
I built these projects while actively pursuing senior data management roles — not as academic exercises, but as focused preparation for the technical leadership challenges I'll face in my next position.
An agentic AI assistant for merchandise financial planning
Built a working prototype that demonstrates conversational AI applied to retail planning workflows. The agent interprets natural language queries, executes planning operations, and generates insights — showcasing the intersection of domain expertise and emerging AI capabilities.
What I built:
• Conversational interface for merchandise financial planning
• Integration with Claude API for natural language understanding
• Women's Sportswear Spring 2025 planning dataset
Technologies: React, Claude API, Oracle RPAS domain modeling
Skills Demonstrated: Product ownership, agentic AI design, domain-to-technical translation
Why this project: Demonstrates my ability to evaluate emerging technologies (generative AI) and translate domain complexity (retail planning) into intuitive user experiences.
Building a university financial data warehouse from scratch
Designed and implemented a complete star schema data warehouse for university financial reporting. Created semantic layers, optimized queries, and connected Tableau for self-service analytics — directly applicable to the enterprise data management challenges in higher education.
What I built:
• Star schema with 5 dimension tables and 1 fact table
• Semantic layer with views and materialized views
• Tableau dashboard connected to governed data sources
• 1,000+ transactions across 3 fiscal years
Technologies: PostgreSQL, Docker, pgAdmin, Tableau
Skills Demonstrated: Dimensional modeling (Kimball), SQL proficiency, semantic layer design, data governance
Why this project: Prepared me for Director-level data management roles in higher education by building hands-on expertise in dimensional modeling, SQL optimization, and semantic layer design.
These projects demonstrate capabilities directly applicable to senior data and product leadership roles:
• Defining product vision and translating it into working prototypes
• Managing end-to-end delivery from concept to functional demo
• Balancing user needs with technical constraints
• Dimensional modeling and star schema design
• SQL proficiency and performance optimization
• Semantic layer design for self-service analytics
• Modern data stack (Docker, PostgreSQL, cloud-native patterns)
• Converting domain complexity into user-friendly interfaces
• Creating governed data products for non-technical users
• Documenting technical concepts for diverse audiences
• Self-directed mastery of new technologies and methodologies
• Rapid skill acquisition in emerging domains (generative AI, modern data warehousing)
• Proactive career development aligned with market opportunities
These aren't just technical exercises — they're strategic investments in my professional development:
Expanding Beyond Retail: While my career has deep roots in retail planning systems, I'm building transferable expertise in data management and AI-augmented workflows that apply across industries — particularly in higher education, non-profit, and enterprise technology.
Hands-On Learning: I learn by building. These projects gave me the opportunity to go from conceptual understanding to practical implementation, strengthening skills I'll bring to my next role.
Demonstrating Adaptability: Technology evolves quickly. These projects show I don't just keep up — I actively experiment with emerging tools, modern architectures, and industry best practices.
Interview-Ready: Both projects give me concrete examples to discuss in interviews — from data modeling decisions to AI integration strategies to product ownership challenges.