[ETR #73] Your Data Project's Catch-22


Extract. Transform. Read.

A newsletter from PipelineToDE

Hi past, present or future data professional!

After choosing a dataset, one of the most significant decisions you must make when creating displayable work is: How am I going to build this thing?

For some, you may try to “vibe code” along with an LLM doing the grunt technical work. If you choose this approach, be warned: Nearly half of all “vibe code” generated contains security vulnerabilities and that’s before you even consider its ability to run. Others may immediately jump into an IDE, confident in their existing skills like Python.

These two examples illustrate a conflicting decision you must make from the outset: Are you optimizing for “showing off” strong, existing skills? Or do you want to signal to an employer you can learn and employ new skills on the fly?

This is the data practitioner’s Catch-22. Do you opt for a sure bet, a familiar tech stack, or do you take a risk and showcase an emerging or “hot” technology that might not be among your core strengths?

Perhaps the biggest determining factor for learning vs. showing off is your time constraint. How much time can you dedicate to your build? If you’re working slowly over a few months, it might make sense to try to implement a new approach.

If a deadline like graduation is rapidly approaching, you might want to stick to tried and true methodologies. There’s absolutely no shame in creating Matplotlib visualizations instead of using a BI tool like Tableau. It might not be as “pretty” but if you nail the business fundamentals, your work could outshine the slickest dashboards.

To remove the guesswork from that critical decision, when to learn and when to rely on existing skills, you need a professional execution framework.

This framework is the foundation of my new ebook resource, and I'd like to offer you a free sample today. It gives you a clear, documented plan to take any project from ideation to deployment without wasting time on dead ends or pondering Catch-22 scenarios.

Thanks for ingesting,

-Zach Quinn

Extract. Transform. Read.

Reaching 20k+ readers on Medium and nearly 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.

Extract. Transform. Read. A newsletter from PipelineToDE Amid layoff announcements from Meta, Amazon and even UPS, it's job aggregator Indeed that signals a different concern for entry-level data job seekers. This week a post on Blind revealed Indeed’s plan to quietly reduce junior roles. They’re not necessarily going to stop hiring or layoff juniors (though they are losing 1300 employees by end of year)—they’re just going to stop paying attention to them. Specifically, Indeed will no longer...

Extract. Transform. Read. A newsletter from PipelineToDE Hi past, present or future data professional! I want to share the single most important realization I had back in the summer of 2021. I was burned out, juggling two part-time jobs, trying to plan a wedding, and drowning in full-time job applications. I felt overwhelmed and underprepared as I plunged into a sea of candidates I perceived to be more intelligent and better "fits" than me. My portfolio was full of the usual Titanic, Iris,...

Extract. Transform. Read. A newsletter from PipelineToDE Hi past, present or future data professional! One of the most validating and terrifying professional moments is reaching the final interview round. It is in this context that you meet candidacy’s final boss, who incidentally, usually ends up being your boss' boss. Specifically I’m referring to the department executive responsible for bringing in additional headcount, i.e. you. While this may sound intimidating, the role of the executive...