Analytics Engineering Toolkit
Reusable patterns for turning operational data into reliable reporting models and decision-ready metrics.
Independent software developer
Data systems that make AI useful.
I build analytics and data engineering foundations for teams that need reliable pipelines, trusted metrics, and a practical path into applied AI/ML.
Project Work
Public writeups and repositories that show how I approach data reliability, engineering tradeoffs, and applied AI/ML experiments.
Data Pipeline Observability
A lightweight monitoring approach for batch data pipelines with run metadata, freshness checks, and failure triage.
Applied AI Experiment Notes
Small experiments exploring retrieval, evaluation, and automation patterns for AI-assisted analytics workflows.
Latest Notes
Writing about data engineering practice, analytics systems, and the move from robust data work into useful AI/ML.
What I Look For in a Reliable Data Pipeline
A practical checklist for freshness, lineage, observability, and operational ownership in modern data pipelines.
Moving from Analytics Engineering Toward Applied AI
How analytics and data engineering experience transfers into AI/ML work, especially around evaluation and data quality.