[ETR #77] Think DE is Hard? Try Going Part-Time


Hi past, present or future data professional!

As time in 2025 dwindles, I wanted to share what I learned about optimizing design, development and troubleshooting time while working 3 days per week this fall.

Quick background: If you’ve been a long-time reader, you’ll know that in March my wife and I had our first child.

Consequently, through my employer, I was eligible for several months of parental leave. Anticipating my wife’s return to work (after much needed time off!) I allocated the second chunk of my leave to take Thursdays and Fridays off from the end of summer until the beginning of this month.

So I became a part-time data engineer in order to be a full-time dad.

Aside from the obvious perks, I found completing tasks in 3 days to be its own challenge. In a perfect dev scenario (in which I actually complete a build in a week.. which is rare), my week looks like this:

  • Monday - Assigned/pick up task
  • Tuesday - Initial development
  • Wednesday - Test & QA
  • Thursday - Refactor/submit “clean” code as a GitHub pull request (PR); if approved, merge
  • Friday - Monitor run; NEVER MERGE ON FRIDAY

The condensed version became:

  • Monday - Pick up task, try to get POC or something functional by the end of the day
  • Tuesday - Tweak and immediately begin testing; if everything looks good, submit a PR
  • Wednesday - Final QA/test; merge and hope I don’t break prod while I’m out

A 5-day dev schedule allows for “breathing room” and reduces the “time boxes” (time constraints) you impose on a more constrained timeline.

While you may not find yourself in my exact situation, I believe I was able to maintain my full-time pace due to efficient strategies you can (and should) steal if you have limited dev time due to school or another job.

  • I never started from 0: Being in a more mature data org means that for most processes some bits already exist; do your due diligence to see if some framework or code exists before starting from scratch
  • I already knew my data: I’ve been an SME on the subscription domain for a while now; knowing the definition of fields and quirks of vendor UIs meant I didn’t spend much time in the discovery phase
  • I templatized/modularized code whenever possible: This is a best practice but it also saved a ton of time. Explore concepts like using environment variables to pass dynamic queries to Docker base images
  • I wrote myself notes: I ended each week with a handoff doc shared with my manager; it provided a starting point for the next working day
  • I leveraged AI but didn’t let it write all my code: I mostly used AI to summarize API documentation and troubleshoot; if I was feeling adventurous, I’d let it generate a first draft of a Python script or SQL query

Most who learn programming obsess over optimization of code, but if you really want to be efficient, start focusing on optimizing your process.

Thanks for ingesting,

-Zach

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