[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

Medium | LinkedIn | Ebooks

Extract. Transform. Read.

Reaching 20k+ readers on Medium and over 3k learners by email, I draw on my 4 years of experience as a Senior Data Engineer to demystify data science, cloud and programming concepts while sharing job hunt strategies so you can land and excel in data-driven roles. Subscribe for 500 words of actionable advice every Thursday.

Read more from Extract. Transform. Read.

Hi fellow data professional! Once, during a virtual interview, I had to nod politely as my interviewer apologized for coughing after their cigarette. Oh, and to make this situation even more cringe—they were driving. Some industries design stressful interview processes to psychologically test a candidate’s poise under pressure. Luckily (for the most part) the software engineering field is not included in this basket of high-stress tests. Sure, we are subjected to moderate stress in the form...

Hi fellow data professional! On a recent holiday, a family member and I were strolling along a beach, talking about AI disruption (relaxing, I know). He, an attorney, assured me his job was AI-proof and jokingly offered to hire me when AI takes my data engineering job. If you ask executives at most companies, they’d find several flaws in that argument. Over 80% of technical executives, including Chief Data Officers and Chief AI Officers, consider data engineering to be an essential role...

Hi fellow data professional! Ken Jee, who you heard from last week, drops some sobering career advice in one of the earliest editions of AI Survival Guide: Making a senior-level tech role is no longer about advancement; it’s about survival. The post talks about the growing moat or "wall" between those breaking into the industry, those in entry-level roles and those in a mid-career phase. In the spirit of AI Survival Guide’s advice to bridge the gap separating the early and mid-career...