Featured project
Dumadine
Multi-tenant hospitality operating system built for live venue conditions, event-driven workflows, and future automation and AI-assisted operational support.
Current work • Founder-led product engineering, architecture, and system design • TypeScript / React / Node.js / PostgreSQL / Drizzle ORM / WebSockets / AWS
Context
A multi-tenant hospitality operating system shaped around live venue operations rather than isolated software features.
Why it mattered
Built an operational core that can support reporting, automation, and operator tooling from one source of truth instead of a patchwork of side systems.
What I owned
- Product architecture across ordering, kitchen flow, inventory, accounting, and venue operations
- Backend and data-flow design for connected operational state
- System structure that leaves room for reporting, automation, and AI-assisted operator tooling
Constraints
- Live-service pressure, shared devices, and real venue workflows
- Domain boundaries that cannot collapse into feature sprawl
- Real-time coordination without turning the product into state chaos
What I changed
- Structured the product around coherent operational state instead of disconnected screens
- Preserved modular domains so downstream automation and reporting can remain trustworthy
- Built the foundation for summaries, support context, and event-driven follow-on workflows
Selected work
PRIVV
Product engineering work for a capital project platform with complex financial workflows, dense planning interfaces, and high trust requirements around system behavior.
Product platform work • Frontend and product engineering on high-trust planning workflows • React / TypeScript / Vite / Tailwind CSS v4 / TanStack Query / Zustand
Context
A capital project platform with dense planning, budgeting, forecasting, and invoicing workflows where trust in system behavior matters.
Why it mattered
Made a dense planning product easier to evolve without letting financial workflow complexity turn into permanent frontend drag.
What I owned
- Frontend and product engineering across complex financial planning interfaces
- UI structure and state handling for dense, high-interaction workflows
- Maintainability and usability improvements as workflow complexity increased
Constraints
- High-trust financial workflows with low tolerance for confusing behavior
- Dense tables, planning views, and forecasting interactions
- Growing product complexity that could easily collapse into frontend sprawl
What I changed
- Reworked frontend structure to better support large, interdependent planning surfaces
- Improved state and data flow across budgeting, scheduling, invoicing, and forecasting views
- Strengthened the product foundation so more complexity could be added without repeated churn
Selected work
DataGPT AI
Product engineering for an analytics system where AI-assisted workflows, data flow clarity, and trust in system behavior mattered as much as raw capability.
Product workflow work • Product engineering on analytics and AI-assisted workflow surfaces • React / TypeScript / Data visualization / Analytics UX
Context
An analytics product where AI-assisted workflows only work if users can understand query flow, result state, failures, and recovery paths.
Why it mattered
Improved trust in the product by making pipeline state, recovery paths, and AI-assisted outputs easier to inspect and verify.
What I owned
- Product engineering across orchestration and analysis interfaces
- UX structure for pipeline state, result explanation, and recovery behavior
- Trust-oriented design for AI-assisted output flows
Constraints
- Opaque workflow behavior quickly destroys user trust
- Complex pipeline state has to be visible enough for users to act on it
- AI-assisted output needs explanation, not just presentation
What I changed
- Built interfaces that made query progress, result state, and pipeline behavior legible
- Surfaced failure and recovery states instead of hiding them behind generic loading behavior
- Treated explanation and trust as core product requirements
Selected work
Experience Digital
Cross-functional engineering work spanning legacy modernization, backend delivery, infrastructure automation, and cloud data platform architecture across application and platform layers.
Cross-functional engineering work • Cross-functional software, platform, and delivery engineering • Next.js / React / TypeScript / tRPC / Prisma / Docker / Terraform / AWS / Azure / Grafana / Prometheus / Loki / Power BI
Context
Cross-functional engineering across legacy modernization, backend delivery, DevOps, and cloud data platform work.
Why it mattered
Proved end-to-end range across product code, delivery systems, observability, and cloud data work without losing engineering clarity.
What I owned
- Product engineering across legacy and modern web application surfaces
- Delivery and platform work across CI/CD, infrastructure automation, and observability
- Contribution to cloud data and reporting architecture across AWS and Azure
Constraints
- Legacy systems and database realities that could not be ignored
- Delivery and infrastructure work that directly affected release speed and reliability
- Moving across product, platform, and data concerns without losing clarity
What I changed
- Modernized application surfaces while preserving compatibility with existing constraints
- Improved performance and delivery flow through better frontend, backend, and platform discipline
- Expanded into infrastructure and cloud data work that supported reporting and operational visibility