Skip to main content

How to prepare for the Data Engineering Interviews?

In recent years, due to the humongous growth of Data, almost all IT companies want to leverage the Data for their Businesses, and that's why the Data Engineering & Data Science opportunities in IT companies are increasing at a rapid rate, we can easily say that Data Engineers are currently at the top of the list of "most hired profiles" in the year 2021-22. 

And due to huge demand companies wants to hire Data Engineers who are skilled in programming, SQL, are able to design and create scalable Data Pipelines, and are able to do Data Modelling. In a way, Data engineers should possess all the skills that Software engineers have and as well as skills Data Analysts to possess. And, in interviews also the companies look for all the skills mentioned above in Data Engineers.

Checkout the 5 Key skills Data Engineer need in 2023

So in this blog post, I am going to cover all the topics and domains one can expect in Data Engineer Interviews

A. Programming Round

Most of the Product based companies, especially MAANG (Meta, Apple, Amazon, Netflix & Google) look for candidates who are extremely good in coding and how well and optimized code Data Engineers can write. So typically the first round for these companies is solving coding questions. Although the level of coding questions would range from Easy to Medium. This round could be an online coding question or whiteboard coding asked in an interview.


B. Technical Round

There could be a first technical round where interviewers want to see whether are you are clear in basic concepts required for any Data Engineering jobs or not. So this round can be full of trivial Programming, Data Structures, Distributed Systems, Data Pipelines & SQL questions. It is not necessary to answer all the questions right but you should be able to answer most of the questions correctly. And you should always answer them briefly without going into much in detail due to time limitations. 


C. System Design Round

Apart from the basic conceptual-based questions, companies also want to know how much you know about Data Engineering. So questions about Data Pipelines, ETL Pipelines, Data Processing Frameworks like Hadoop, Spark, Beam, etc would be asked. You should be able to clearly explain how would you design, create and maintain reliable and fault-tolerant pipelines for a huge volume of data. You should be able to answer questions related to Big Data. Check out more about that in Top Big Data Interview Questions


D. HR/Behavioural Round

Almost all companies conduct these types of interviews to see if the candidate can communicate well, express his thoughts and ideas well, and if he is a good fit for the team and organization. In this round, you can expect typical HR questions like why do you want to join this company, why do you want to leave your current job, why should we hire you, etc. You can also expect some behavioral questions like tell me your last project which you are proud of, tell me where you deal with conflicts within the team, etc. For these rounds, it is better to prepare beforehand, write and practice before appearing for an interview.

Good Luck with the Interviews!!

Comments

Post a Comment

Popular posts from this blog

How to migrate the data between AWS and Google Cloud Platform

There are several ways to migrate data between Amazon Web Services (AWS) and Google Cloud Platform (GCP). Here are three common approaches: Use a Cloud Data Integration Tool: Both AWS and GCP offer a range of tools that can help you move data between the two platforms. For example, AWS Data Pipeline is a fully-managed data integration service that can extract data from various sources, transform the data as needed, and load the data into a destination system. On GCP, Cloud Data Fusion is a similar tool that can help you build, execute, and monitor data pipelines between various data sources and destinations. You can use these tools to create a data pipeline that moves data between AWS and GCP. Use a Command-Line Tool: Another option is to use a command-line tool, such as aws s3 cp or gsutil, to transfer data between AWS S3 and GCP Cloud Storage. For example, you can use aws s3 cp to copy data from an S3 bucket to your local machine, and then use gsutil cp to upload the data to Cloud ...
Post a Comment
Read more

Difference between Union and Union All in SQL

You might be using Union or Union All in your SQL code while doing Data Analysis or building Data Pipelines. Ever wondered what is the difference between them and how using one over another can be more efficient? Yes, there is a small yet significant difference between Union and Union All. Let's look at that by understanding each of them individually. 1. Union All  Union All basically allows you to concatenate the table that has a similar structure of tables. The important condition to have Union All of the tables is that both the tables should have the same number of columns. So when you take Union All of two tables what it does in the background is it directly joins the tables without removing duplicates or redundant records.   2. Union  Union is also similar to Union All except one difference that it removes the duplicates records before taking the Union of the tables.  There is one disadvantage of Union over Union All, that since it removes duplicated records bef...
Post a Comment
Read more

What is Shuffling in Spark

Shuffling in Spark is a mechanism that Re-Distributes the data across different executors or workers in the clusters.  Why do we need to Re-Distribute the data?    A) Re-Distribution is needed when there is a need of increasing or decreasing the data partitions in the situations below: When the partitions are not sufficient enough to process the data load in the cluster When the partitions are too high in numbers that it creates task scheduling overhead and it becomes the bottleneck in the processing time. Re-Distribution can also be achieved by executing the shuffling on existing distributed data collection like RDD, DataFrames, etc by using the "Repartition" and "Coalesce" APIs in Spark. B) During Aggregation and Joins on data collection in Spark, all the data records belonging to aggregation or join should reside in the single partition and when the existing partitioning scheme doesn't satisfy this condition there is a need to re-distributing the data in in...
Post a Comment
Read more