Most important skills to crack the Data Engineer Interview as a Fresher

Introduction

Nowadays, data engineering is one of the fastest-growing jobs across all the data science jobs. So, everyone wants to move or start their career in data engineering.

Recently, I cracked a Data Engineer Associate position at TD Bank in Canada, So, I hope I'm qualified enough to share about data engineering interview experience.

I would like to start this blog with one of the biggest mistakes people make while preparing for DE interviews, especially freshers who try to learn all the technologies and advanced skills of data engineering as a fresher which is a totally wrong method to prepare for any job role.

For instance, I’ve seen students learning multiple cloud technologies as a fresher thinking that it will help them to find a job easily. But the reality is that if you master any one cloud instead of learning multiple clouds then it will help you to find a job in an even faster way.

So, as a fresher instead of learning all the technologies used in data engineering one should learn fundamental concepts of data engineering. So, let's make a list of some of the most important skills for an entry-level data engineer.

Important skills for entry-level Data Engineers.

To make a list of important skills for a DE interview, there are tons of articles available on the internet, But the problem I faced was that in every single skill, there are plenty of topics to learn about. So, the problem is to choose what to learn in that particular skill.

For example, Python is the most used programming language in data engineering. But the problem is that in Python, there are hundreds of topics to learn.

So, the main question is what are some important concepts of Python one should learn to Ace a DE interview?

So, instead of making a list of all skills required to ace a DE interview, we should also make a list of all the concepts of those skills which are important to ace an interview.

Python

Python is the most popular and important programming language in the field of data science as python is widely used to create data pipelines, integrations, automation, and clean and analyze data.

Let’s list down some must-do topics of Python for DE interviews.

1. Input/Output operations.
2. Command line Arguments.
3. Data Types: String, List, Tuple, Set, Dictionary, List Comprehension, and Dictionary Comprehension.
4. Operators: Conditional, Mathematical, and Logical.
5. If-Else and Nested If-Else.
6. For & While Loop.
7. Functions: **kwargs (pass key-value arguments to function), return multiple values from the function
8. Exception handling.
9. File handling
10. Modules.
11. Lambda Function.
12. Most Important libraries of Python: (Pandas, Matplotlib, NumPy, JSON, CSV, re (Regular Expressions), and sys.

DBMS

Before learning SQL, one should know all DBMS concepts which will help to create reliable database schemas.

Here are some DBMS topics for DE

1. ACID Properties.
2. Transactions.
3. Concurrency Control
4. Deadlock
5. Indexing
6. Hashing
7. Normalization forms
8. Views
9. Stored Procedures
10. ER Diagrams

SQL

SQL is the most important skill to master when you are preparing for data engineering interviews. Because in Data Engineering almost all the Big Data Frameworks (like Spark and Hive) offer some flavor of SQL to process data.

Let's list down some most IMP SQL topics for DE.

i) Basic Level Concepts of SQL:

1. All commands of DDL, DML, and DCL.
2. Integrity Constraints.
3. Primary key, foreign key, super key, candidate key, unique key, composite key, and alternate key.
4. Where Clause: Logical Operators, Conditional Operators, Like, Between, is Null and is Not Null.
5. Joins Inner Join, Left Join, Right Join, Full Outer Join, and Conditional Operators in join conditions.
6. Case-When Statement.

ii) Intermediate Level Concepts of SQL:

1. Group By: Simple Aggregation Functions, Concat with Group by, Case-When with Group By.
2. Working with Nulls.
3. Date Functions.
4. Regexp.
5. Substring Functions.
6. Coalesce Function.

iii) Advanced Level Concepts of SQL.

1. Sub-Query: Single-row subquery, multiple-row subquery, multiple-column subquery, correlated subquery, and nested subquery.
2. Lookups: In, Not In, Any, All, Exists, Not Exists.
3. With Clause
4. Union, Union All, Intersection.
5. Window Functions: Over Clause, Row Number, Rank, Dense Rank, Sum, Count, Min, Max, Avg, Stddev, Lag, Lead, First_Value, Last_Value, Nth_Value, Ntile, Row Between Frame Clase, Range Between Frame Clause.
6. Pivot Tables.

Data Warehousing:

Data Warehousing is one of the most IMP skills for data engineers to master as most of the time data engineers' task is to create ETL pipelines to load data into the data warehouse and create a data platform for data scientists and data analysts for further analysis of data.

Here are some must-do topics of data warehousing for data engineers.

1. The main use of Data Warehouse.
2. OLAP vs OLTP.
3. Dimension Tables.
4. Fact Tables.
5. Star Schema.
6. Snowflake Schema.
7. Warehouse Designing Questions.

Big Data Terminologies:

There are some Big Data Terminologies or concepts which you should be aware of before going to learn Big Data Frameworks like Hadoop.

In every data engineering interview, interviewers must ask some questions regarding big data to check candidates' understanding of big data.

There are some questions listed below that you should know before any DE interview.

1. What is Big Data?
2. 5 Vs of Big Data
3. Distributed Computation
4. Distributed Storage
5. Vertical vs Horizontal Scaling
6. Commodity Hardware
7. What is Cluster?
8. Different File Formats: CSV, AVRO, JSON, Parquet, ORC.
9. Different Types of data: Structured Data, Semi-Structured Data, and Un-Structured Data.

Big Data Frameworks:

After Learning SQL and Python now it's time to learn Big Data Frameworks which are the main tools where data engineers spend most of their time.

There are three main Big Data Frameworks that are most important for data engineers.

1. Apache Hadoop
2. Apache Spark
3. Apache Hive

Apache Hadoop:

Apache Hadoop is the most used open-source Big Data Framework to store and process big data in a distributed fashion.

Important topics of Hadoop for DE Interviews.

1. Apache Hadoop Architecture (Most Important).
2. HDFS
3. Map-Reduce (Only Architecture Understanding and the difference between map-reduce and spark, no need to learn more about map-reduce. Because it’s outdated and replaced by Apache Spark.)
4. YARN

Apache Spark (Most important for interview):

Apache Spark is a distributed computing engine that can process big data up to 100 times faster than map-reduce.

There are three main components of spark for DE interviews.

1. Spark Core
2. Spark SQL
3. Spark Streaming
4. Architecture Understanding of Spark is most Important for interviews.

Apache Hive:

Apache hive is a data warehousing framework built on top of Apache Hadoop to analyze big data stored in HDFS.

Must-do Topics for interviews.

1. Load data in Different File Formats
2. Internal vs External Tables.
3. Partitioning.
4. Bucketing.
5. Different types of joins: map-side join, sort-merge join
6. User-defined functions.
7. SerDE in Hive

After learning all the skills mentioned above you are qualified enough for cracking entry-level data engineer interviews.

Project Walkthrough

It’s very important for every data engineer to be able to explain their project for cracking any DE Interviews.

So, there are many DE projects available on YouTube that you can make and learn from it.

I have provided links to some of the great projects of Data Engineering.

Basic Data Engineering Projects

Data Engineering Project on Cloud:

Cloud Technologies:

Learning cloud technologies is an asset for entry-level data engineers.

So, you can learn any cloud technologies from Azure, GCP, and AWS.

Conclusion

Hopefully, the above thoughts help when it comes to preparing for DE interviews. There are plenty of good resources out there when it comes to preparing.

If you learn something from this blog, please help me grow by upvoting this blog.

Good Luck! with your next DE interview.

Thank you so much for reading

Happy hunting!

Best of luck with your journey!!!

Follow for more such content on Data Engineering and Data Science.

Resources used to write this blog:

• Learn from Youtube Channels: Darshil Parmar, e-learning bridge, data engineering, GeekCoders, Ankit Bansal, Data Savvy, TechTFQ
• I used Google to research and resolve my doubts
• From my Experience
• I used Grammarly to check my grammar and use the right words.

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