I design and modernize enterprise data platforms, cloud systems and agentic AI solutions — combining architecture, hands-on engineering, governance and production readiness.
High-level, sanitized case studies showing the architecture, engineering patterns and production considerations behind systems I have designed or engineered. Deeper implementation notes, code snippets and selected GitHub examples will be added progressively.
Governed enterprise AI patterns spanning Snowflake Cortex AI products, semantic models, NLQ, search and agents, together with spec-driven AI engineering using MCP, guardrails, hooks and enterprise tool integrations.
View case study →Configuration- and specification-driven orchestration using reusable operators, YAML and SQL patterns, automated validation, CI/CD and observability to standardize pipeline engineering across teams.
View case study →Modern data and lakehouse architecture across S3, Snowflake and Databricks, combining layered data design, incremental processing, semantic access, governance, interoperability and cost-aware engineering.
View case study →Focused technical experiments used to validate an architecture, integration path or engineering pattern before broader enterprise adoption.
Lab work is intentionally labeled so a visitor can distinguish a reference implementation or POC from production-delivered architecture.
Evidence-first investigation across MWAA, CloudWatch and Snowflake, with LangGraph/Bedrock reasoning, governed remediation and a clear path toward MCP-enabled enterprise tooling.
Open Architecture Lab →A reference pattern for exposing enterprise systems to AI agents through narrow, discoverable and policy-controlled tools rather than unrestricted application access.
Reference implementation directionA practical evaluation pattern for measuring diagnosis quality, unsupported claims, human overrides, latency, token usage and LLM cost before scaling an agent workflow.
Experiment / future labLong-form architecture briefs explaining design choices, trade-offs, engineering patterns, governance and lessons for production systems.
An eight-step governed architecture spanning ingestion, open data, Snowflake, Databricks, trusted data products, semantic intelligence, RAG, agentic applications and enterprise operations.
Read Article →A practical architecture view of AWS Kiro, Claude Code and Codex through enterprise modernization, MCP, engineering context, execution, governance and production guardrails.
Read Article →A practical enterprise architecture for investigating workload failures using evidence collection, historical incident correlation, root-cause reasoning and governed remediation.
Read Article →The major capability areas I bring together when architecting and modernizing enterprise data platforms, cloud systems and production AI.
I bring 20+ years of experience designing and modernizing enterprise technology, data platforms and cloud solutions. My work combines architecture direction with hands-on engineering across Snowflake, Databricks, Airflow, Python, SQL, cloud services, automation and production operations.
My current focus includes Agentic AI, RAG, MCP, AI-assisted engineering and governed enterprise AI — connecting emerging AI capabilities with production-grade data, security, governance, observability and cost discipline.
I work best where architecture must move beyond diagrams: shaping the target design, making trade-offs, setting engineering standards, helping teams implement it and driving the solution toward production readiness.
I’m interested in conversations around enterprise architecture, modern data platforms, cloud modernization, Agentic AI and production AI engineering.