Cheap Tokens Won’t Make You a Better Engineer
AI usage may become cheaper or more expensive, but engineering value still comes from solving the right problem within real constraints.
Read article →Ideas for building reliable data systems
Field notes about data engineering, architecture decisions, Agentic AI, and the lessons behind a career spent modernising data platforms.
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Why I’m Breaking the DRY Rule: The Case for Data Duplication
Data duplication is not automatically technical debt. Sometimes separate copies are the clearest way to meet different latency, scale, and analytical requirements.
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I Stopped Googling. Is AI Search Actually Honest?
AI answers feel cleaner than search results, but recommendations can still be shaped by training data, commercial integrations, and advertising.
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I Thought My Data Engineering Career Was Over in 2017
Being assigned to a legacy Informatica project felt like failure. It became the foundation for the engineering career I wanted.
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From Informatica to dbt: Journey to Modern, Maintainable Data Pipelines
How dbt answered a question I first asked while building Informatica PowerCenter mappings: why can't data teams manage transformations as SQL and software?
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Should We Always Build Decoupled Systems?
Kafka, Pub/Sub, and queues are powerful, but asynchronous messaging is not the default answer for every interaction. Start with the business requirement.
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