Summary
Data engineer who publishes security research. I proved regex safety filters are algebraically blind (peer-reviewed, 2026) and built data pipelines handling 10,000+ datasets with zero consumer disruption. Five years across financial services — migrations, streaming, agentic systems. I like hard problems and tend to document what I learn.
Research & Side Projects
Algebraic and Computational Limits of LLM Guardrails
Proved regex safety filters are algebraically blind to modular-position encodings via syntactic monoid analysis. 91 production patterns analyzed, 34/35 aperiodic. Peer-reviewed, 2026.
Experience
Financial Services (Milwaukee, WI)
- Wiring an acquired institution's personal loans product into the parent platform — real-time data feeds, payment processor history, GraphQL data product interlinking
- Designed an Airflow + config-as-code pipeline that moved 10,000+ payment processor datasets during post-M&A integration; full audit trail throughout
- Replaced shell script ETL with production-grade infrastructure from scratch — 250 dataset migrations, zero consumer disruption; release velocity jumped from 1–10 to 20–25 datasets per cycle
- Modernized 3 Databricks batch jobs covering 32 compliance datasets; config-driven validation toolkit cut dataset validation from 3 min → 30 sec (~62 hours manual work eliminated)
- Docker runtime workflows across 7 repos — saved 14+ person-days in 2 weeks; one of 7 engineers selected for org-wide AI coding pilot
- Developed a spec-driven development methodology — 66% velocity increase, adopted org-wide
Financial Services (Milwaukee, WI)
- Shipped credit-freeze data flow end-to-end across a distributed decision system
- Built a test-vetted UI for credit policy modelers — cut prototyping time from days to hours
- Found and fixed a Kafka consumer test suite that was passing without ever validating data — those tests were blind for months
- Led PySpark 2→3 migration of a production AWS Step Function ETL pipeline with no downtime
Financial Services — Data Products / Platform
- Built a LangGraph multi-agent POC for developer onboarding — handles API discovery, schema comprehension, and code generation in a single workflow; cuts "how does this API work" from hours to minutes
- Designed and ran a Prompt Engineering Bootcamp — 2 sessions, 15 engineers, 7 patterns; published all materials as open-source teaching resources
- Mentored 2 junior devs — both shipped production-ready workflows independently within a month
Financial Services (Milwaukee, WI)
- Built data streaming pipelines, implemented a DMN rules engine, and drove schema modernization across multiple platforms
- Designed multitenancy schema and data exhaust architecture for ETL pipelines
Skills
AI/Agentic: LangGraph, Multi-Agent Architectures, MCP, spec-driven development
Python: Pytest, Pandas, PySpark, Airflow, LangGraph, Jupyter
Java: Spring Boot, JUnit, Mockito, Microservices, Cucumber, Maven
Go: Concurrency, goroutines, HTTP handlers, CLI tooling
Scala: Spark
Tools: SQL, AWS, GCP, Docker, OpenAPI, Splunk, GitHub, Jira, Confluence, Tableau, Claude Code, Windsurf
Education & Certifications
AWS Cloud Practitioner · GCP Associate Cloud Engineer