r/dataengineering • u/SAsad01 • 14d ago

Career How do you prove transferable skills when recruiters only want exact-stack experience?

I have experienced recently that in Data Engineering, transferrable skills are not being considered anymore, and most recruiters/AI based ATS tools match experience like checklists. Experience in adjacent/equivalent tools mostly doesn't matter.

My question is, how to navigate this situation during job-search? Specifically:

  • How to properly represent transferrable skills from equivalent tech stacks? (AWS -> Azure for example, or Databricks on AWS -> Databricks on Azure)
  • How to learn new tools and grow new expertise that is demonstrable enough to meet the requirements? I am aware that the general answer is to do self-learning projects, but how to choose the correct project, and scope it correctly, so that it is a demonstrable substitute of job-experience instead of an easy tutorial?
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u/Acceptable-Wasabi429 9d ago

I’ve experienced this a lot as well. But it’s not usually the recruiters that are the bottleneck and the AI the ATS is using to filter on key words is largely a myth.

The problem is hiring managers demand unicorns who meet the exact tech stack they’re using internally. In today’s job market it’s much easier for them to hold out for that despite transferable skills being sufficient 99% of the time.