[ETR #94] Your Project Is Missing A "Time Box"


Hi fellow data professional!

This edition almost became an apology because I’ve been on a tight deadline and pre-baby morning wake up thinking/writing time has become GSD (get sh!t done) hour.

Long story short: I got brought in late to a time-sensitive project that required me to speed through a planned pipeline migration.

As a recovering news junkie (aka journalist), I used to live and die by deadlines. But, given the unpredictability of data-oriented work and internal deliverables, it’s actually rare I follow strict due dates for my builds.

But in technical work, deadlines help not only maintain timelines but, more importantly, scope.

In other words, deadlines are a way to manage a task’s complexity. For instance if I agree to a two-week deadline for one pipeline and a stakeholder wants me to fetch more data, I can emphasize the time constraint to keep on task.

Managerial frameworks often govern technical environments. Agile, one of the most popular, thrives on an “iron triangle” of logistic constraints.

  • Scope
  • Time
  • Cost

All of these prevent the temptation of over-engineering a solution. For example, I have multiple professional and personal automations that rely on Google Sheets as their data source.

If I had the time I would build out proper tables for each. However, at the time, that would have increased both the scope and time to achieve a deliverable.

But if you’re reading this and haven’t yet worked a data engineering job, you’ll still want to think about adding self-imposed deadlines for your personal builds to avoid something worse than over-engineering: Perfectionism.

Working in a role where GitHub commits are a badge of productivity reinforces the importance of not tinkering.

Such guardrails allow you to:

  • Getting a minimal viable product (or project) within a reasonable timeframe, even if you literally have nothing but time
  • Develop a consistent work schedule with clear checkpoints
  • Combat burnout by assigning your project a final “release date”

If you don’t work well keeping a month-long deadline in mind then I suggest identifying and assigning “milestones” to your work.

You could have a milestone for your initial idea/business problem all the way through your finished presentation.

For milestones especially, it’s important to be incremental and precise with what you hope to achieve.

Overall, despite my insistence on providing yourself a “time box”, I do think it’s important to be lenient and kind to yourself if you’re making a concerted effort.

You can still set realistic deadlines without pressure.

Read my rare "day turn" experience fulfilling a request for a c-level exec to see exactly how to handle high-pressure deadlines.

Thanks for ingesting,

-Zach Quinn

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! SQL Lite, the database you most likely learned SQL on, is built atop 100k lines of source code. Sound like a lot? Compare that to Chromium, the engine for Google Chrome, which boasts 30+ million lines of code under the hood. Shortly after acquiring Twitter/X, the world's first trillionaire, Elon Musk, famously asked engineers to tell him how many lines of code they wrote per day, igniting a debate among engineers throughout the software and data domains. When I...

Hi fellow data professional! If you read my note on Tuesday you’ll know I’m coming off of the data engineering week from hell that seeped into my personal life, and delayed the launch of something cool I was planning to share with you; if you want to know more about that, scroll to the end of this message. Last week a flagship data source had a major problem and since it’s within my ownership area, I was the one with the knowledge and responsibility to fix it. I wanted to share the experience...

Hi fellow data professional! Hardly a work day goes by without receiving a request from a data analyst. They range from the mundane “Can you add this column?” to the occasional emergency “The data didn’t load all weekend and the leadership call starts in 15 minutes!” At the end of a jam-packed week I received an unusual request: Help with a Python script. My teammate wanted to know: Best practices How to commit to GitHub What the best way to deploy is They admitted the task was simple,...