Extract. Transform. Read.A newsletter from Pipeline Hi past, present or future data professional! If you haven’t heard "Happy New Year" enough in the past week… let me be, hopefully, the last to say it as we embrace all 2025 has to offer. Beginning a new year comes with the inevitable conception (and ultimately ignorance) of a new year’s resolution. Instead of focusing on one abstract goal to improve, I’d like to suggest, instead, that you form lasting habits, especially when it comes to your technical career. To ensure this advice is relevant to all who graciously read it, I’m splitting my suggested habits into three buckets:
Habits for Job Seekers
There are a lot of negative factors impacting job seekers right now but with the macro economy improving, there is hope for a smoother process in 2025. Habits for Entry-Level Employees
In 2024 I earned my first promotion, slowly hoisting myself up the corporate engineering ladder. Habits for Advancement
The last habit that I suggest you ingrain, regardless of position, is to never take shortcuts. Validating your data is going to take an extra day and delay a release? Not ideal, but neither is releasing a flawed product that would require extra time for debugging and redesign. And remember, when writing your date strings it’s now 2025-01. That’s a habit that will take some adjustment. Before you go: If you’re looking for an end-to-end project with real-world application in 2025, I’d suggest analyzing your personal finances. My first story of ‘25 covers a dashboard I built over the holiday break to track yearly and monthly credit card spending–with zero API connections. Visualizing 48 Months Of Credit Card Spending with PyPDF, SQL & Looker (Pt. I) Thanks for ingesting, -Zach Quinn |
Top data engineering writer on Medium & Senior Data Engineer in media; I use my skills as a former journalist to demystify data science/programming concepts so beginners to professionals can target, land and excel in data-driven roles.
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! From 2014-2017 I lived in Phoenix, Arizona and enjoyed the state’s best resident privilege: No daylight saving time. If you’re unaware (and if you're in the other 49 US states, you’re really unaware), March 9th was daylight saving, when we spring forward an hour. If you think this messes up your microwave and oven clocks, just wait until you check on your data pipelines. Even though data teams...
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! As difficult as data engineering can be, 95% of the time there is a structure to data that originates from external streams, APIs and vendor file deliveries. Useful context is provided via documentation and stakeholder requirements. And specific libraries and SDKs exist to help speed up the pipeline build process. But what about the other 5% of the time when requirements might be structured, but...
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! To clarify the focus of this edition of the newsletter, the reason you shouldn’t bother learning certain data engineering skills is due to one of two scenarios— You won’t need them You’ll learn them on the job You won’t need them Generally these are peripheral skills that you *technically* need but will hardly ever use. One of the most obvious skills, for most data engineering teams, is any...