Felt important,Bookmarked it in study folder......(Never opened the folder since I created it ) More on reddit.com

You can check out It covers all of Azure for data engineering. https://www.coursera.org/specializations/microsoft-azure-dp-203-data-engineering?

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I interview data engineers for a mid size SaaS in Europe, our data team size is about 20 people. IMO there are 2 dimensions to a DE technical skill set: Software Engineering and Architecture. Most of the work is debugging/developing data pipelines and evolving the architecture. There is a fair amount of infra work, maintaining things like Airflow, Kubernetes etc. I usually don't pay much attention to specific tools, because the tools skills transfer well if you understand the concepts and experience transfers very little. (e.g. if you have experience with some specific Airflow peculiarities, that doesn't translate to Dagster). So to evaluate the software engineering skills, I look for things like data-oriented design, separation of concerns, testability. Specifically we ask the candidates to write some Python and SQL code, you'd be surprised how poor some peoples' basic Python skills are. In the architecture round I'm looking for the ability to think in high level concepts, i.e. how the data flows, who the stakeholders are, what tradeoffs are available and what pros/cons they have. Red flags are if the candidate doesn't know basic Python, e.g. writes a `for a in range(0, len(b)):` instead of `for a in b`, doesn't know basic libraries like `requests`. With SQL, if they don't know what CTEs are, how HAVING works, etc. In the Architecture round, if they speak about what tools they would use instead of what problem they are trying to solve, e.g. "I'll just do everything in Spark". More on reddit.com

Just take them all and place in a publicly accessible google doc that can only be modified by you. Problem solved. More on reddit.com