[ETR #19] 1 Question New Data Engineers Can't Ask


Extract. Transform. Read.

A newsletter from Pipeline

Hi past, present or future data professional!

I recently participated in a technical design meeting that was derailed by a single, fundamental question.

“Why?”

Despite the fact that I worked with the particular data source we were discussing for nearly two years, I fell into the common trap of going “on autopilot” and failing to question the initial need for the data. At this point, you would think asking “why” of years’ worth of work would be offensive.

Instead of myself or other team members getting defensive, it led to a productive conversation about not just refining our approach to ingestion, but also inspired talk of how we can manage stakeholder expectations and softly encourage them to “do more with less.”

Fortunately, you don’t need to derail a meeting to leverage what I call a productive why. Asking occasional, tactful “whys” can position you as a critical thinker and thought leader (or at least an enthusiastic thought contributor) within your org. When appropriate, consider asking…

  • Why are we using x tool over y when x clearly offers a more streamlined integration with our data warehouse?
  • Why are we dedicating development resources to solving this issue when there isn’t a clear business outcome?
  • Why are stakeholders asking for a new data pipeline when this existing table provides nearly all of the dimensions they’re seeking?
  • Why are we paying for x service when we could feasibly build our own solution?

I realize you may not be in a professional role; nonetheless, I’ve found a lot of value can result from occasionally asking “why” even when you’re simply writing code.

For instance, I was a habitual user of Pandas’ .append() method. Unfortunately, to my disappointment, Pandas 2.0 deprecated .append() in the past year. I easily could have panicked and said “Iterating and appending key values to an empty data frame is how I’ve always converted JSON to a data frame. What am I going to do?” But being forced to adapt to the change made me think about what prompted that habit initially.

To learn what that motivation was plus how a simple "why" nearly left me tongue-tied in an interview, read the latest on Pipeline.

And so you don’t have to question where those hyperlinks go, here they are as plain text.

Questions? zach@pipelinetode.com

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 Hi past, present or future data professional! If you’re a job seeker in the data space, your GitHub portfolio has only one job: To act as a calling card that gets you to the next step of the hiring process. Too often, I review portfolios for potential referrals and see brilliant code buried under structural mistakes that have nothing to do with programming skill. Your GitHub is not just cloud storage for your code; it’s a public display...

Extract. Transform. Read. A newsletter from PipelineToDE Hi past, present or future data professional! Despite crushing autocorrect scenarios, most AI code assistants like CoPilot miss a critical step when helping developers of any experience level: Validation. Arguably, leveraging an AI Agent to validate a code’s quality is on the user. But a surprising amount of experienced programmers are taking the worrying approach of believing an AI’s first “thought” when it comes to code that will...

Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! A data science manager recently gave me some blunt, liberating advice over coffee: “If a team lead really cares what cloud technology you know (AWS, GCP, etc.) and doesn’t consider transferable experience… run.” This critical advice, which informs the conclusion of my soon-to-be-released ebook on data engineering project development, cuts to the core of a major problem in data hiring: The...