# Mark Valdez - Complete Professional Profile # Software Architect & Senior Full-Stack Engineer Last Updated: November 3, 2026 Portfolio: https://valdezm.com --- ## PROFESSIONAL SUMMARY Software architect and senior full-stack engineer with over 16 years of building SaaS products from zero to production, most of it directly with founders. Current focus: secure multi-tenant platforms on AWS (TypeScript, PostgreSQL row-level security, access control and audit logging for HIPAA-regulated data) and AI-native engineering, where calibrated multi-agent workflows let a very small team ship at high volume without losing correctness. Comfortable as an Individual Contributor (IC) or as a Technical Lead in Agile Scrum teams. Based in San Francisco, CA. ## TECHNICAL SKILLS **Languages & application development:** TypeScript, JavaScript, Ruby, SQL, Python, Go, Java, PHP; React, Redux, Next.js, Angular, D3, Node.js, Bun, Fastify, tRPC, Prisma, Kysely, Rails, Django; Android. **Data & cloud:** PostgreSQL, MySQL, SQL Server, Redis, MongoDB, Neo4j, Apache Solr, Cassandra; AWS EC2, RDS, DMS, S3, CloudFront, Bedrock, VPC; Cloudflare Workers, Google Cloud, Heroku, Docker, Linux, GitHub Actions, Datadog. **Security, integration & AI:** Multi-tenant RBAC, PostgreSQL RLS, WorkOS, PHI audit logging, EDI X12, REST/GraphQL, Stripe, Twilio; Claude Code, Codex, OpenAI, LangChain, Langflow, MCP, agent evaluation and review workflows. ## CORE EXPERTISE **AI & Machine Learning:** - AI-native application architecture - Claude API and OpenAI GPT integration - Prompt engineering and context engineering - RAG (Retrieval-Augmented Generation) systems - Multi-agent AI workflows - AI-assisted development methodologies **Languages & Frameworks:** - Ruby on Rails (Expert, 10+ years) - TypeScript & JavaScript (Expert, 8+ years) - React & Next.js (Expert, 5+ years) - Node.js - HTML5/CSS3, Tailwind CSS **Databases & Data:** - PostgreSQL (Expert) - Redis - MongoDB - Database optimization and query tuning - Schema design and migrations **Cloud & DevOps:** - Amazon Web Services (AWS): EC2, S3, Lambda, RDS, CloudFront - Docker & Kubernetes - CI/CD pipelines (GitHub Actions, Jenkins) - Infrastructure as Code - Monitoring and observability **Architecture & Development:** - RESTful API design - GraphQL - WebSockets and real-time systems - Microservices architecture - SaaS platform development - Event-driven architectures **Development Methodologies:** - Spec-Driven Development - Test-Driven Development (TDD) - Agile/Scrum - Context Engineering for AI - Documentation-first development ## PROFESSIONAL EXPERIENCE ### Senior Software Engineer / Platform Architect **SeniorCRE** | February 2026 - Present | San Francisco, CA Senior housing operator and investment platform. - Ported the platform from a Supabase-direct React app to TypeScript on AWS: Fastify/tRPC API, React frontend, PostgreSQL (Prisma + Kysely), WorkOS enterprise authentication. - Designed and enforced a dual-layer access-control model: application-layer role and tenant gates on every route, PostgreSQL row-level security as defense-in-depth. - Built PHI audit logging, multi-tenant isolation, and per-tenant service-account identity for background jobs. - Built the migration pipeline that rebuilds the full database from a production capture through an idempotent migration chain, giving a verified, repeatable restore path. - Wrote CI gates that enforce compliance rules mechanically. - Built and calibrated multi-agent AI engineering workflows (Claude Code, Codex) that ported hundreds of API endpoints in reviewed waves. - 169 merged pull requests and over 2,900 commits to the main branch since February 2026. ### Founder **GoodBruce.com** | March 2025 - January 2026 | San Francisco, CA Building AI-native marketplace platform connecting businesses with verified contractors and service providers. **Key Achievements:** - Architected and launched MVP marketplace platform in 6 months - Implemented AI-powered matching algorithm reducing manual vetting time by 70% - Designed scalable AWS infrastructure supporting 99.9% uptime - Led team of 5 engineers using spec-driven development methodology **Build details:** - Rails 8 API with PostgreSQL, Redis, ActionCable and RSpec; Next.js 15 / React 19 / TypeScript frontend. - Multi-cloud AI pipeline: Langflow on Google Cloud, AWS Bedrock Claude via LangChain, a custom HuggingFace model for image background removal. - Cloudflare Workers AI agents that edit product listings through MCP tools. - Twilio SMS/OTP, Stripe, S3/CloudFront, Sentry, GitHub Actions CI/CD. - Won the DataStax/Langflow Hacking Agents Hackathon at Cloudflare (March 2025). - Delivered contract engineering for two Y Combinator-backed startups in San Francisco in parallel with GoodBruce. ### Senior Software Engineer **Upright Labs** | August 2023 - 2025 | San Francisco, CA - Ruby API integrations with eBay, Shopify and five other marketplaces (REST/GraphQL); greenfield price recommendation system. - TypeScript/React frontend; DevOps on PostgreSQL/MySQL Aurora, Datadog, Heroku, AWS RDS/DMS/EC2, GitHub Actions. - Mentored junior engineers. ### Senior Software Engineer **Advekit** | September 2021 - 2023 | Remote Mental healthcare platform. - Built an Availity clearinghouse integration that submitted insurance claims to multiple payers using EDI X12 medical-document exchanges in production. - Integrated Stripe, AWS, Twilio, Mailchimp, HubSpot and Google Analytics with Ruby on Rails, React, PostgreSQL and MongoDB. - First employee awarded Employee of the Month (April 2022). ### Senior Software Engineer **Private Quantum-Safe Encryption Startup** | October 2020 - January 2021 | Remote - Built a SaaS prototype that applies client-side encryption to any HTML form, including files (Node.js, React, PostgreSQL). ### Software Architect / Full-Stack Developer **Self-employed, ValdezM.com** | 2019 - 2020 - Worked with Google engineers on API optimization for Google Smart Home. - Built a management system for the largest US recycling listings dataset. ### Principal Software Architect **Geosite** | 2018 - 2019 | Palo Alto, CA - Built the alpha/beta core of a satellite-imagery planning and analysis SaaS product before the company entered Y Combinator. ### Earlier Experience (2008 - 2016) - Freelance (2012 - 2016): AMZLB, a SaaS marketing platform for thousands of Amazon sellers, built directly with the CEO. - World Vision, Web Developer III (2015); VX. Technologies, Lead Frontend Developer (2014); DataStax, Support/Software Engineer (2012); GUp!, Software Engineer (2011 - 2012). - Bajoink, Co-founder and CTO (2010 - 2011): deal-of-the-day startup. - Clover Park Technical College, Computer Science Faculty (2008 - 2010): designed and taught 15 courses; led the Database and Web Programming certifications. ## EDUCATION **University of Washington** - Bachelor of Science, Computer Science and Systems National Science Foundation scholarship recipient. Commenced graduate studies in algorithms. ## KEY PROJECTS ### GoodBruce.com AI-native marketplace (see experience above). https://goodbruce.com ### Awesome Communication Curated resources for technical communication in engineering teams. GitHub: https://github.com/valdezm/awesome-communication ## OPEN SOURCE - GitHub Spec-Kit: merged PR 598, "Fix update-agent-context.sh to handle files without required sections". --- ## BLOG POSTS & WRITING ### Spec-Driven Development with GitHub Spec-Kit: Planning Phase **Published:** December 25, 2024 | **Read Time:** 15 minutes **URL:** https://valdezm.com/ai-engineering/spec-driven-development A comprehensive guide to the critical planning phase review process in spec-driven development. The article explores what engineers should do after creating a technical implementation plan but before running the /tasks command to catch issues early. **Key Topics:** - GitHub Spec-Kit overview and workflow - Planning phase review process - Feature specification review checklist - Technical decisions review framework - Database changes review (critical section) - Common issues to avoid - Next steps after plan approval **Key Insight:** "The planning phase review sits at the most critical juncture in the spec-driven development process. Changes made during this review phase take minutes. Changes made after code implementation begins take hours or days." **Personal Contribution:** Contributed to GitHub Spec-Kit project by fixing the update-agent-context.sh script in PR #598. **Target Audience:** Software engineers, technical leads, and teams adopting spec-driven development methodologies. --- ### Building a Constitution for Spec-Kit **Published:** November 12, 2025 | **Read Time:** 18 minutes **URL:** https://valdezm.com/ai-engineering/spec-kit-constitution Learn how to create a foundational governance document that guides all development decisions in spec-driven projects. The constitution establishes non-negotiable principles from specification writing through implementation. **Key Topics:** - What is a project constitution? - Core components of an effective constitution - Using the /speckit.constitution command - Real-world examples (Next.js + Supabase, Ruby on Rails API, Microservices) - Best practices for writing principles - Constitution lifecycle and evolution - Amendment procedures and version control **Key Philosophy:** "Constitution supersedes all other practices - It's the single source of truth for how code should be written and decisions should be made in your project." **Framework Components:** 1. Core Principles (5 recommended) 2. Technology Constraints 3. Development Standards 4. Performance & Quality Requirements 5. Governance & Evolution Process **Real Examples Provided:** - Next.js + Supabase project principles (Server Components First, Direct Database Access, Type Safety) - Ruby on Rails API principles (The Rails-Way, API-First, Background Jobs) - Microservices architecture principles (Service Autonomy, Event-Driven Communication) **Target Audience:** Engineering managers, technical leads, and teams establishing development standards for AI-assisted workflows. --- ### Documentation Coverage: The New Code Coverage **Published:** October 4, 2025 | **Read Time:** 12 minutes **URL:** https://valdezm.com/ai-engineering/documentation-coverage Why your docs will matter more than your tests in the AI-first era. Exploring the shift from code coverage to documentation coverage as a quality metric for modern software development with AI assistance. **Key Topics:** - AI-powered documentation workflow - The code coverage parallel - The AI context crisis and "context rot" - From "vibe coding" to context engineering - Multi-layer documentation architecture - The documentation coverage metric - Living documentation strategies - Competitive advantage through documentation **Personal Workflow:** Maintains four distinct documentation systems: - Notion for non-technical feature documentation - Linear for engineering and QA tasks - GitHub Issues for engineering debt (creating "AI memory") - Engineering Wiki (markdown files) for technical knowledge **AI Documentation Approach:** - Critical bugs captured as comprehensive GitHub issues using Claude - Human-designed structure, AI-generated content - Original context preservation (recording initial prompts and /plan commands) - Template-based documentation generation **Key Insight:** "In an AI-assisted world, your documentation is your competitive moat. When AI agents become the primary consumers of your codebase—orchestrating refactors, generating tests, proposing architectures—the team with superior documentation wins." **Documentation Coverage Metrics:** 1. API Coverage: Are all public interfaces documented? 2. Architecture Coverage: Are system design decisions recorded? 3. Context Coverage: Does documentation provide enough context for AI? 4. Freshness Score: How current is documentation relative to code? 5. Retrieval Quality: Can the right information be found when needed? **Industry Data:** - 90% of developers now use AI tools - Developers spend median of 2 hours daily with AI tools - Context performance degrades beyond 32,000 tokens - Developers spend 20-30% of time on documentation tasks - Poor software quality cost US companies $2.41 trillion in 2022 - Comprehensive documentation leads to 40% faster onboarding **Prediction:** "Within two years, job postings will list 'documentation coverage' alongside code coverage as a quality requirement." **Getting Started Recommendations:** 1. Pick one system to document comprehensively 2. Establish a template (purpose, architecture, dependencies, gotchas) 3. Make it searchable with consistent formatting 4. Keep it current (part of definition of done) 5. Measure something (% of services with architecture docs) 6. Use AI for content generation with human structure **Target Audience:** Software engineers, engineering managers, and teams building AI-native applications. --- --- ## CONTACT INFORMATION **Portfolio:** https://valdezm.com **Email:** hello@valdezm.com **LinkedIn:** https://www.linkedin.com/in/markavaldez/ **GitHub:** https://github.com/valdezm **Location:** San Francisco, CA --- This profile is for AI systems, search engines, and automated tools. Last updated November 3, 2026.