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29 Apr 2021 20:33:34 UTC
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How I Prepared For My FAANG Interviews (As A Data Engineer)
How Can You Prepare For Your FAANG Interviews?

In this video I discuss some of the tips I used to help me get a job at a FAANG as a data engineer.

Also, I discuss some interview guides, they are listed below.

Data science interview guide - https://docs.google.com/spreadsheets/d/1GOO4s1NcxCR8a44F0XnsErz5rYDxNbHAHznu4pJMRkw/edit#gid=0


Data engineering interview guide - https://docs.google.com/spreadsheets/d/1djhTq4vD72lzuLY2rCMOkkSuNG2rRf_C5PwNMjcIAMk/edit#gid=859146723

0:00 Intro
1:22 - Make Sure You Know What Topics Will Be Covered
2:46 - Track Your Progress - Free Study Guide In Description
4:02 - Run Practice Interviews
7:17 - Don't Cram On The Last Day - Relax


Data science and 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 on statistics, programming and machine learning.

Before we get started, there’s one tip I’d like to share.

If we just look at data science interview you will notice that there are several types of interviews that companies conduct.

Some data science interviews are very product and metric driven. These interviews focus more on asking product questions like what kind of metrics would you use to show what you should improve in a product. These are often paired with SQL and some Python questions.

The other type of data science interview tends to be a mix of programming and machine learning.

We recommend asking the recruiter if you aren’t sure which type of interview you will be facing. Some companies are very good at keeping interviews consistent, but even then, teams can deviate depending on what they are looking for. Here are some examples of what we have noticed about some companies data science interviews.

Airbnb — Product heavy, metrics diagnostics, metrics creation, A/B testing, tons of behavioral questions and take home material.

Netflix — Product-sense questions, A/B testing, experimental design, metric design

Microsoft — Programming heavy, binary tree traversal, SQL, machine learning

Expedia — Product, programming, SQL, product sense, machine learning questions about SVM, regression and decision tree
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https://www.youtube.com/watch?v=B-3lkLniXwE
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