Building systems that connect data, technology, and real-world outcomes.
Senior Data Engineer at TextNow. University of Waterloo graduate. Exploring distributed systems, AI infrastructure, and measurement platforms.
I build systems.
I think in systems.
I explore systems.
I'm a Data Engineer with experience building large-scale measurement and analytics platforms. My work spans data engineering, distributed systems, advertising technology, experimentation, and AI.
I enjoy solving difficult problems where business, technology, and human behavior intersect. The messiest problems, the ones that span teams, disciplines, and systems, are the ones I find most interesting.
Outside of work, I write about technology, read widely, and think about what it means to build things that matter. I'm working toward technology leadership and, eventually, building something of my own.
Areas of Interest
Things I've built and am building.
A mix of production systems, research, and side projects.
Sleep and Activity Dashboard for Senior Housing
Privacy-preserving monitoring system using environmental sensors to identify behavioral changes and potential health concerns in senior residents, without cameras or invasive wearables.
Architecture
Edge sensors → MQTT → Stream processing → Anomaly detection → Alert system → Dashboard
Smart Park Lighting System with Real-Time Fault Monitoring
End-to-end IoT data pipeline for smart street lighting — automatic intensity control via PIR and LDR sensors, real-time fault detection across LED arrays, and environmental monitoring (temperature, humidity, AQI) streamed through MQTT into an ELK stack hosted on GCP.
Architecture
Sensor nodes (nRF/ESP8266) → MQTT broker → Logstash ingestion → ElasticSearch → Kibana dashboards → X-Pack alerting
LLM Systems Playground
Personal research environment for experimenting with retrieval-augmented generation, agent systems, vector databases, and model serving infrastructure. Where ideas become prototypes.
Architecture
Vector DB → Embedding pipeline → LLM orchestration → Agent framework → Evaluation harness
Ideas I think about out loud.
AI Literacy or AI Theatre?
Organizations measure AI adoption through dashboards and usage metrics — but experienced practitioners write one detailed system prompt and get it done. High prompt volume is not a signal of competency.
Don't Aim for the Stars
Every idea seems taken. The gaps are closing. So what's left for us? A reflection on ambition, the cost of easy validation, and why the horizon beats the stars.
May 21, 2026Born AI-First vs. Bolted-On Later: Which Codebase Actually Wins?
Greenfield AI projects ship fast but abstract away edge cases. Legacy codebases have context but create review bottlenecks. Neither pure approach wins — here is what actually works.
May 14, 2026The "Lazy" Genius: Why Your AI Code Reviewer Needs a Promotion
Current AI code reviewers re-analyze entire codebases for minor fixes. The fix is making them strategically lazy — tracking state, mapping blast radius, and knowing what actually changed.
May 8, 2026Docstrings in the Age of Agents
Docstrings now serve two incompatible audiences — humans and AI agents. What reads as helpful detail to a person becomes noise to an agent. The solution is deliberate separation.
Apr 30, 2026What I'm doing right now.
Currently Working On
Currently Reading
Currently Exploring
Current Goals
Let's build something interesting.
Whether you want to talk about data systems, AI infrastructure, a project collaboration, or just have an interesting idea. I'm always up for a good conversation.