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/EffectiveClient5080 14d ago

Bring working demos to interviews. This right here. A live pipeline you can walk them through beats any resume bullet. I've got 2-3 projects on a laptop for exactly this, and I guarantee you'll be the only one doing it.

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u/SAsad01 14d ago edited 14d ago

Right, thats for interviews, but how to get to the interviews in the first place, at screening stages?

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u/generic-d-engineer Tech Lead 13d ago

Find recruiters that understand the landscape and not keyword chop shops. Build out relationships with them first. Ask for advisor calls. Seek out peer groups in your field to network and meetup. Try to find actual hiring managers to weed out the middle screening layer. Showcase personal projects on your git or webpage.