About
I've always been drawn to the how behind data — how it's architected, engineered, and ultimately put to work. These days that curiosity is pointed at AI systems end to end: the pipelines that feed them, the models wired into products as real features, and the interface layer people actually touch.
This site is proof of that work as much as a description of it — it runs on the infrastructure: a Postgres-backed content system, a RAG chat widget grounded in what's actually published here, a public MCP server, and a handful of scoped agents that write to it directly.The build log has the real story behind each piece, mistakes included.
If you're navigating the AI-data terrain too — whether you're standing up your first pipeline or deep in the weeds of an existing one — I'd love to help however I can.
What I do
Data pipelines
Ingestion, transformation, and orchestration that hold up under real production load — the unglamorous plumbing that decides whether anything built on top of it can be trusted.
A dbt Semantic Layer Over My Own GitHub Activity →AI integration
Wiring Claude and other models into products as features, not demos — tool use, structured output, and small agents scoped to one job, each held to the same audit trail as a human editor.
An Agent Toolkit for Running the Site →Applied AI features
The interface layer on top — the part your users and customers actually touch. Grounded in what's actually published, cited, and honest about what it doesn't know.
Retrieval-Augmented Chat: Ask This Site a Question →Skills & tools
- Data Engineering & Warehousing
- Big Data & Pipelines
- Multi-Cloud Platforms
- LLM & AI Integration
- AI Orchestration & Observability
- Retrieval & Vector Search
- Business Intelligence & Analytics
- Machine Learning
- Application & Cloud Engineering
Also published elsewhere
- Qlik Cloud Data Integration: Data Movement Architecture — white paper on QDI's data movement architecture ↗
- Automate Your Machine Learning with Qlik Talend Cloud Data Pipelines — AutoML + data pipeline integration ↗
- Using Qlik Talend Cloud to Find Answers — RAG-style Q&A over pipeline data ↗
- Inject AI into Your Databricks + Qlik Talend Cloud Data Pipeline — AI-in-pipeline pattern with Databricks ↗