7 pages

Engineering Judgment

Practical essays on making sound software and data engineering decisions under uncertainty, including tradeoffs, risk assessment, YAGNI, and responsible AI use.

Engineering judgment is the work that remains after tools make implementation faster. These articles examine how to frame a problem before coding, test assumptions, assess operational risk, resist premature abstraction, and decide where AI-generated output still needs human scrutiny.

They are written for experienced software and data engineers who make decisions with incomplete information and remain accountable for production outcomes. The focus is not on collecting patterns or rules, but on understanding tradeoffs well enough to choose what fits the actual situation.

When code becomes cheap, design has to become easier to review

AI can generate implementation faster than a team can understand it. The answer is not less review, but making the problem, constraints, and expected behaviour easier to examine before code arrives.

AI-Assisted Development Software Design Engineering Judgment Staff Engineering

Shaping with AI

Software solutions do not start with code

I can move from idea to code faster with AI, but the real work still starts before that: asking better questions, spotting constraints, and building the right mental model.

AI-Assisted Development Engineering Judgment

Shaping with AI

Switching from doing to directing

As AI takes over more of the mechanical work, engineers at every level need to learn how to direct, verify, and own what gets shipped. This article reflects on why that mindset should start early, not only at senior level.

AI-Assisted Development Staff Engineering Engineering Judgment

Shaping with AI

How I used Cortex Code Desktop to assess the risks in Snowflake's upcoming behavior change bundle

Snowflake rolls out semantic and breaking changes through behavior change bundles. Before a bundle becomes the default, there is a window to test how those changes might affect your workloads. In this article, I show how I used Codex and later Cortex Code Desktop to assess the risks, identify likely breakpoints in our Airflow and dbt pipeline, and map out likely fixes before the bundle lands.

Snowflake AI Agents Engineering Judgment

Shaping with AI

Effectiveness anti-patterns — part 6: YAGNI: don’t abstract before the second use case

A practical YAGNI story from data engineering: keeping a CI workflow intentionally narrow until the second real use case appeared.

Engineering Effectiveness Engineering Judgment Code Quality Pipeline Orchestration Staff Engineering

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