I build data systems that people can trust.
My work sits at the intersection of data engineering, cloud architecture, distributed systems, data quality, open source, and transportation analytics.
I am a data engineer with more than a decade of experience designing, building, and operating data platforms and analytics systems. My work focuses on reliable cloud data systems, large-scale processing, change data capture, data quality, and the engineering required to keep production pipelines dependable.
I am especially interested in the problems that appear after a system leaves the architecture diagram: late-arriving data, schema evolution, idempotency, incremental processing, observability, failure recovery, performance, and cost.
A recurring theme in my work is that a data platform should not only move data quickly. It should make it possible to understand where the data came from, whether it is complete, whether it can be trusted, and what happens when something goes wrong.
I also have a strong interest in transportation and public-safety analytics. That gives me a second lens for engineering: data quality and reliability matter because the systems we build can ultimately support real-world operational and safety decisions.
This website is where I publish technical essays, architecture lessons, open-source work, and engineering ideas that can be shared publicly without exposing proprietary systems or data.
What I work on
- Cloud data architecture and large-scale data engineering
- AWS, Spark, distributed processing, and Apache Iceberg
- Change data capture and incremental data pipelines
- Data quality, observability, and reliability engineering
- Open-source software and engineering collaboration
- Transportation and road-safety analytics
How I approach engineering
I prefer systems that are understandable, observable, testable, and recoverable. Performance matters, but so do correctness, maintainability, operational simplicity, and the ability to explain why a system behaves the way it does.