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The Databricks Data Engineer

Jakub Lasak

Short episodes on the Databricks skills that get data engineers hired and promoted. One theme per episode, built for a commute: how senior engineers make the calls, what interviews actually test, and the career moves that change your level. By Jakub, a Databricks Data Engineer (ex-Uber).

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Avsnitt

31 avsnitt

  1. Two Databricks jobs, one Delta table, same moment: 3 endings (and the 1 that needs a human) 11:41
  2. Unity Catalog Can't Manufacture Trust: Your Most-Read Databricks Table Might Be the Least Trusted 11:55
  3. Your Databricks director: what they fear, why they fund the duller proposal, what gets yours through 11:41
  4. Data skew is a checkout line - how to unjam Databricks jobs without bigger clusters 12:08
  5. Can You Reach Senior Databricks Engineer Without Ever Leading an Architecture Review? 11:11
  6. Why Databricks serverless exists, and which of your jobs should stay on classic clusters 12:03
  7. How Databricks engineers lose the final round (and the 4 places it happens) 10:44
  8. The real reason your Databricks week never got simpler - it isn't the lakehouse 11:39
  9. The Databricks incident where the most senior engineer never touched the keyboard 11:48
  10. 9 Databricks decisions that reveal your real seniority (and one that only looks senior) 11:41
  11. The Databricks job market in 2026: there are two of them, and only one answers 11:02
  12. Databricks broadcast joins: when the memo beats the meeting (and when it kills a task) 10:48
  13. Build the platform or use the platform: the two Databricks data engineer tracks nobody names 11:47
  14. Why Unity Catalog exists: the Databricks governance chaos it was built to end 10:34
  15. How to get credit for Databricks platform work without becoming the on-call martyr 11:10
  16. Why Databricks Built Delta Lake When Parquet Was Already Good Enough 9:39
  17. The Databricks pipeline that ran green for a year and was wrong the whole time 8:29
  18. How Photon actually makes your Databricks queries faster (and when it silently doesn't) 10:46
  19. The Databricks interview round nobody studies for (and almost everybody fails) 11:36
  20. The Spark Shuffle is baggage claim: why your job waits instead of computes (and more workers won't fix it) 11:09
  21. Your Databricks data quality framework is a Yeti: everyone talks about it, nobody has seen it work 11:42
  22. Why senior Databricks engineers write less code than mid-level ones 11:08
  23. 4 habits that quietly turn your Databricks Delta Lake into a swamp 11:31
  24. Liquid Clustering vs Z-Ordering: 4 questions that decide 18:11
  25. The compounding curve: why some Databricks engineers' salaries grow 5x faster than others 22:44
  26. The 90/9/1 rule of Databricks performance work - how to triage Spark optimization in 60 seconds 17:21
  27. The Databricks data engineer in 2026 - the four shifts that just changed your job 18:30
  28. 9 Behaviors Quietly Killing Your Promotion To Senior Databricks Data Engineer 14:09
  29. The Dashboard Theater: What Databricks Engineers Build That Nobody Opens 15:48
  30. The Preparation Gap: What Interviewers Actually Evaluate in Databricks Data Engineers 15:36
  31. The Invisible Engineer: Why Your Best Work Gets the Least Recognition 15:06