What I Learned From 100+ Data Engineering Interviews - Interview Tips

Data engineering interviews are tough.

Interviewing for any technical position generally requires preparing, studying, and long, all-day interviews.

Data engineering interviews, like other technical interviews, require plenty of preparation. There are a number of subjects that need to be covered in order to ensure you are ready for back-to-back questions.

Some positions require Hadoop, others SQL. Some roles require understanding statistics, while still others require heavy amounts of system design.

We have gathered many of the resources that we have used to study and get jobs at companies in the FAANG family as well as other major tech companies. We have yet to find one that requires you to know anything about Hadoop during the interview, so that has not been included in this study guide.

What I Wish I Knew Before I Became A Data Engineer https://www.youtube.com/watch?v=FvCInKiLJVg

How I Got My First Data Engineering Job https://www.youtube.com/watch?v=FgPKHzXRhcE

If you want to help support the channel and you were going to sign up for Udemy or Datacamp anyways, then consider checking out the courses below.

Data Engineering Bootcamp - Datacamp http://datacamp.pxf.io/0Jm43J

Data Warehousing Basics - Udemy https://bit.ly/3wg94E2

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Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio

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About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consults on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.

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