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.
[ETR #105] Why Employers Care About A Green Square
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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 first started as a data engineer I assumed more code = more features. And more logging, error handling and sophistication. I stared in awe at SQL scripts that were 1000+ lines, believing this was the mark of a true data architect. I quickly learned I was wrong. In most use cases, the volume of code you write does not matter. Except one. Your git commit history. While the number of lines of a given script hardly matters (but let’s make it legible, please), your GitHub commit history reveals a compelling metric: Consistency. With AI coding solutions cranking out as many lines of code as you can beg them for, employers are less interested in how much you can code and more intrigued by how often you commit. Aka how many little green squares you're putting up per week. Sure, consistency reveals a baseline dedication. But it also demonstrates an ability to push through difficult “blockers” to reach solutions functional enough to display, contribute or deploy. Programming advocates realized the power of consistency. And you can see it in community initiatives from Advent of Code, encouraging 20+ days of commitment, to Koans, a Pythonic approach to iterative brain teasers with a zen motif. As a former student I know how frustrating it can be to just read a book or complete a color by numbers coding exercise. That’s why I’ve created a premium, comprehensive GitHub repo with 100+ data sources, utility scripts and optimization patterns. It also contains an 8 module course taking you from data analyst to foundational data engineering concepts. The goal isn’t just for you to commit “the solution.” It’s to encourage consistent, iterative development. Click here to let me know you want early access on 6/30. In closing, a BoJack Horseman quote summarizes incremental progress of coding or any iterative challenge. “It gets easier. But you have to do it every day. That’s the hard part.” Thanks for ingesting, -Zach Quinn Medium | LinkedIn | Ebooks |
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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.
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,...
Hi fellow data professional! It finally happened. I fell for a job scam. Luckily I realized my naivety after responding to the initial email. But let’s back up. We’ll examine Why this particular attempt was so “real” What made me skeptical How to prevent this from happening to you Established professionals in any field have the privileged problem of receiving unsolicited recruiter inquiries. If it’s from a random firm I typically move it to junk; if it’s a big name company, I give a look...