I'm looking for insights from Data Engineers, Architects, or Managers at HashedIn by Deloitte:

Are Data Engineering projects focused on development, support, or operations?

Which cloud platforms are most used—AWS, Azure, or GCP? Is AWS truly more common?

Are many Data Engineers on the bench? If so, is it due to project availability or other reasons?

How hard is it to get Azure-based projects? Does AWS dominance limit Azure-focused opportunities?
Your insights would be greatly appreciated!


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Even in our company many data engineers are free as once a project goes live happens you don't need so many people as databricks have automated a lot of stuff..gone are the days when data warehouse project team size used to be of 8-10 poeple and this next budding data engineers are not realising. Too much if automation has made work simpler in data bricks..and same is happening in AI...currently maximum AI projects are data based so u dont need to many people again...i see a trend that AI engineers are eventually working as data engineers and that is leading to more craning in DE space..we also reduced our data team size from 16 people to 6 ..

Ok thanks. But my question was something different

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Hi fishes,
Hope you are doing well. I am looking for a job having 3+ years of experience in Data Engineer domain/ Python Development. If your organization is recruiting for this position. Then let me know or forward my profile to your connections.
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NP - 30 days
Skills - Python, SQL, AWS, Pyspark, Jenkins
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