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The Data Engineering Interview Pitfalls No One Talks About (But Many Make)
“I solved the coding problem. I answered the SQL question. I even mentioned Spark… but I didn’t get the job.”
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That’s what a mentee told me after a recent data engineering interview.
He wasn’t a beginner. He had years of experience, solid tools on his resume, and even shipped pipelines in production. But when the interview feedback came back, it was a pass.
And that made me pause.
Because this wasn’t just about him.
It’s about a pattern I’ve seen over and over again — especially in 2025, where AI tools, flashy platforms, and endless learning resources are more accessible than ever… yet fundamentals are often forgotten.
Let’s talk about why good candidates miss out on great opportunities — and what you can do differently.
👨🏽💻 Meet Arun: A Talented Engineer with a Blind Spot
Arun had 5+ years in the field.
He’d written PySpark code, scheduled workflows in Airflow, and deployed pipelines on cloud platforms.
He knew his tools: dbt, Databricks, Snowflake, Azure.
