The Databricks Data Engineer

Jakub Lasak
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Helping 18k+ Databricks data engineers become seniors: interview like seniors, execute like seniors, think like seniors.

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  1. The Preparation Gap: What Interviewers Actually Evaluate in Databricks Data Engineers

    5 天前

    The Preparation Gap: What Interviewers Actually Evaluate in Databricks Data Engineers

    She could explain shuffle hash join versus sort merge join. She knew when Adaptive Query Execution kicks in. She had six weeks of notes on Delta Lake, Spark memory, and cluster configs. She walked into the Databricks senior interview feeling genuinely confident. Then the interviewer asked her to walk through a diagnosis, not recite a definition, and everything she studied was aimed at the wrong target. In this episode: - Why senior Databricks interviews test judgment, not knowledge, and what that means for your prep - The exact moment an interviewer recategorizes you from senior candidate to mid-level - How a debugging scenario exposes the preparation gap in under sixty seconds - Three examples of flipping memorization into scenario-based practice with the same material - The self-assessment that tells you whether your study ratio is working against you This episode is for Databricks data engineers who've been studying hard for a senior interview but aren't confident it's the right kind of preparation. Whether you're building your first study plan or revising one that didn't work last time, you'll walk away knowing exactly how to shift your prep toward what interviewers are actually scoring. --- Helping 18,000+ Databricks data engineers become seniors: interview like seniors, execute like seniors, think like seniors. Follow The Databricks Data Engineer for new episodes. LinkedIn: linkedin.com/in/jakublasak Newsletter: dataengineer.wiki #DataEngineering #Databricks #DataEngineer #CareerGrowth #ApacheSpark #DeltaLake

    16 分鐘
  2. The Invisible Engineer: Why Your Best Work Gets the Least Recognition

    3月30日

    The Invisible Engineer: Why Your Best Work Gets the Least Recognition

    She kept a terabyte-scale pipeline running for six months without a single incident. Not one page, not one late dashboard. Then review season came and the engineer who spent two weekends fixing an outage he partly caused got the promotion instead. Her name wasn't in a single incident report, because when you prevent problems, there's no report to put your name on. In this episode: - Why the skills Databricks data engineers are hired for produce structurally invisible output - The pattern behind how promotion rubrics reward firefighters and ignore fire preventers - How to keep an incidents-prevented log that turns "nothing broke" into a track record - The communication shift that makes your manager appreciate flawless execution instead of dismissing it as easy - How to frame infrastructure wins in business-outcome language that shows up in reviews This episode is for Databricks data engineers who consistently deliver solid work but feel overlooked at review time. Whether you're mid-level wondering why flashier peers get promoted faster, or senior and tired of your best infrastructure work going unnoticed, you'll walk away with a concrete visibility system you can start this week. --- Helping 18,000+ Databricks data engineers become seniors: interview like seniors, execute like seniors, think like seniors. Follow The Databricks Data Engineer for new episodes every week. LinkedIn: linkedin.com/in/jakublasak Newsletter: dataengineer.wiki Independent educational resource. Not affiliated with or endorsed by Databricks, Inc.

    15 分鐘
  • 共 2 集

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Helping 18k+ Databricks data engineers become seniors: interview like seniors, execute like seniors, think like seniors.

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