Data & AI Platform Architect | Google Cloud
I design software, data, and AI systems that turn business goals into durable, production-ready platforms. My work connects cloud architecture, data modeling, machine learning, AI agents, and product delivery with explicit operational and governance boundaries.
I work with teams in regulated and data-intensive environments to make complex systems easier to operate, verify, and evolve. That includes data accessibility, MLOps, decision intelligence, platform cost control, and evidence-aware AI workflows.
My primary tools include Google Cloud, BigQuery, Snowflake, Azure, SQL, TypeScript, Python, and Go. I approach architecture as a product discipline: technical choices should support the people, decisions, and long-term operating model around the system.
- Measured Studios explores durable AI systems, data platforms, and interactive software.
- Mainland Dispatch is an evidence-led research notebook for contextual China and U.S.–China coverage.
| Project | Focus |
|---|---|
| xstate-python | Hierarchical Python statecharts with XState/Stately JSON compatibility and SCXML-oriented semantics. |
| Awesome Economic Data | Curated high-frequency and alternative economic indicators with methodology context. |
| Awesome NFL Data | Reviewed NFL data sources, APIs, analytics tools, film resources, and research methods. |
| Awesome NBA Data API | An OpenAPI-driven Flask service for basketball data products. |
| Flask Apps | Maintained reference applications for APIs, dashboards, authentication, and worker-backed architectures. |
- Cloud and data-platform architecture
- Data modeling, analytics engineering, and decision intelligence
- Machine-learning and AI-system delivery
- API, event, and workflow design
- Production reliability, security, governance, and cost control
- Technical strategy translated into measurable product outcomes
- Prefer evidence over implied readiness: source, tests, CI, deployment, and live behavior are separate claims.
- Make contracts executable at system boundaries through schemas, validation, tests, and observable failure modes.
- Keep changes reproducible, dependency-aware, and small enough to review without losing the larger architecture.
- Build tools around the teams and decisions they serve, not around technology for its own sake.
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