Become a Data Engineer at FAANG | Data Engineering Career Roadmap | Crack MAANG | SCALER

In this video, Shashank Mishra (Data Engineer - III, Expedia, Ex-Amazon) walks you through the entire process of becoming a Data Engineer at FAANG companies. Check out our FREE masterclasses by leading industry experts now: https://bit.ly/3J9t7vW

Who is a data engineer?
A data engineer is someone who acts as a gatekeeper and facilitator of data transit and storage. They create data reservoirs and are critical in managing such reservoirs.

What is Data Engineering?
Data Engineering, as a profession is fast gaining pace in the data science ecosystem, and due to the strong need for Data Engineers, even FAANG companies are willing to spend a fortune for qualified individuals.

Coding Interviews at FAANG. Are They Tough to Crack?
Short answer. Yes. Every year, FAANG tops the list of the most sought-after companies for programmers around the globe. The salary packages, the working environment, and additional perks are a few reasons why coders want to ace the interview of these companies. Not to forget, the reputation that the FAANG companies entail as well.

So naturally, when a tech lover decides to pursue a career in data engineering, FAANG companies become an obvious choice. Why?

Global recognition, incredibly high pay packages, a superb learning environment, flexible working hours, and much more are what draw job seekers to FAANG firms.
As a result, getting a job as a Data Engineer in one of these companies is not easy.
FAANG companies receive over a million applications each year, with an approval rate of lesser than 3%.

So, what should one do differently? Here’s a video that will give you a roadmap to becoming a Data Engineer in FAANG.

The episode covers the following topics -

0:00 - Introduction
0:30 - Understanding programming languages
2:24 - Data manipulation libraries
3:25 - Operating systems and scripting
5:56 - Data structures and algorithms
8:22 - Proficiency in DBMS
12:50 - Expertise in data warehousing
13:57 - Big data frameworks
17:56 - Workflow managers
18:57 - Knowledge of NoSQL databases
19:58 - Messaging queue technologies
20:52 - Understanding BI tools
21:54 - Cloud platforms

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