Data Engineering For Beginners
Published: 2023-11-09 19:07:26

**Who is a Data Engineer?**

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Imagine you're opening a small store, and the first thing you'd do is decide what stuff you want to sell and how you're going to get it. Well, big companies with lots of data also need a plan to get their information out of storage and use it effectively. That's where data engineers come in—they're like the behind-the-scenes experts who bring in raw data from different places and get it ready for big business applications. Before we dive into the nitty-gritty of what they do, let's first understand why there's such a demand for these jobs in various industries.

**In-Demand Data Engineering Jobs**
       Ten years ago, being a Data Scientist was considered the hottest job of the century. But here's the twist: the buzz around it might not be as real as we think. According to data from the Data Science Interview Report by interviewquery, the number of job interviews for Data Scientist roles only went up by 10% in 2020. Surprisingly, the interviews for Data Engineering roles shot up by a whopping 40% in the same year. Glassdoor, a job-search website, even kicked Data Scientist jobs from their top spot for the first time since 2016.
        In 2019 and 2020, the number of job positions for Data Scientists stayed pretty much the same. However, other data-related jobs like Data Engineers, Business Analysts, Machine Learning Engineers, and Data Analysts are becoming more in-demand to make up for this leveling off.

As per the report by DICE in 2020, Data Engineer emerged as the fastest-growing job in 2015, with a growth rate of 50% year-on-year

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The other websites also suggest something similar, as can be noted from the mentions below:

Burning Glass Nova Platform reported 88% year-on-year growth.

Hired State of Software Engineer Report revealed a 45% increase in data engineer job roles, again year-on-year.

LinkedIn’s Emerging Job Report for 2020 also presented 33% year-on-year growth stats for data engineer jobs.

Additionally, as more and more companies rely on cloud solutions, there is an urgent need to hire many data engineers to provide essential support to the team of data scientists. According to the website comakeit, the big data and data engineering services market is estimated to grow from 18% per annum in 2017 to 31% p.a. in 2025.

Thus, now is the right time if you plan to transition to a data engineering career from your current job. To get more clarity on the role of data engineers, continue reading the next section that highlights the roles and responsibilities of data engineers.

**What does a Data Engineer do?**
      A data engineer is like the frontline hero when it comes to dealing with a company's most valuable asset—data. Their main job is to make sure that all the different teams in the company can easily dig into the data and use it for whatever they need. They do this by sourcing the data using something called ETL pipelines and making it all nice and easy for everyone in the organization to understand. But that's not all they do—data engineers usually have a bunch of other tasks up their sleeves to keep things running smoothly.

**Role and Responsibilities of a Data Engineer**

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_Prepare, Manage, and Supervise Data Pipelines:_
Get things ready, handle, and oversee the pathways that let data move around efficiently.

_Construct and Launch ETL/ELT Pipelines:_
Build and set in motion these pipelines that start by bringing in data and then handle various data tasks.

_Gather and Manage Data from Various Sources:_
Collect and handle data from different places based on what the business needs.

_Team Up to Create Algorithms for Various Data Stuff:_
Work with a group to come up with step-by-step instructions for how data is stored, collected, accessed, checked for quality, and maybe even used for data analysis.

_Collaborate with Data Scientists and Set Up the Tools for Making Things Better:_
Connect with the data experts and set up the systems needed to figure out, plan, and put into action improvements to how things work inside the company.

_Use Tools like SQL and Big Data Tech to Get Data from Different Places:_
Access different data sources using tools like SQL and fancy Big Data tech to build smart pipelines that move data around.

_Bonus Points for Knowing Tools Like Snowflake:_
Having experience with tools like Snowflake is like having an extra skill in your pocket.

_Create Solutions Focused on Good Data, Smooth Operations, and Other Cool Features:_
Build answers that make s**ure the data is top-notch, everything runs smoothly, and other special features that describe the data.
Write Scripts and Solutions to Move Data Between Different Spaces:

**Data Engineer Salary**

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As the demand for data engineers keeps going up, so do the salary expectations for this role. Data engineering is not just rewarding in terms of job satisfaction but also financially. Let's take a look at the average annual salaries for data engineers in some major countries around the world:

In the United States, the average annual salary for a data engineer is approximately $115,157. This is notably higher than the average earnings of a Data Scientist ($101,995) or a Software Engineer ($93,965).

In India, the average annual salary for a data engineer is ₹10,70,746.

Data engineers in the United Kingdom earn an average annual salary of £48,481.

Down under in Australia, a data engineer can expect an average yearly compensation of A$110,000.

Over in Germany, data engineers bring in an average income of €64,702 per year.

In Russia, a Data Engineer can anticipate an average yearly income of 2,24,492 PP.

After learning about the enticing job description of a data engineer and the attractive salary figures, you might be curious about what skills you need to jump onto the data engineering bandwagon. We'll delve into that in the next section.
**Data Engineer Skills**
      Here is a concise list of technical skills required to become a big data engineer. You will also find a sample project idea to help you grab these skills in the most practical manner and ace your next data engineering interview.

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**Passion/Enthusiasm for Data-Driven Decision Making**
Fall in love with your data; your data will love you back. Yes, it’s that simple. To start with data engineering, you need the right mindset to learn it. And by the right mindset, we simply mean the desire to learn something new and challenging. The art of curating valuable inferences using data is not that old and has only recently reached an exciting peak. So, it is likely that you will encounter problems that will demand extra effort, but if you have strong willpower, you can easily ace this domain.

**Structured Query Language or SQL (A MUST!!):  Learn to Interact with the DBMS Systems**
Many companies keep their data warehouses far from the stations where data can be accessed. The role of a data engineer is to use tools for interacting with the database management systems. And one of the most popular tools, which is more popular than Python or R, is SQL. So, ensure that you are well-versed in various SQL commands, syntax, and use-cases for deducing.

**Knowledge of a Programming/Scripting Language**
You won't have to spare extra time, but you must practice at least one programming language - Java or Python as most data engineers require them in their day-to-day activities. The role of a big data engineer involves analyzing data with simple statistics and graphs. A data engineer relies on Python and other programming languages for this task. 

**Understand the Fundaments of Cloud Computing**
Eventually, every company will have to shift its data-related operations to the cloud. And data engineers are the ones that are likely to lead the whole process. Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure are the three top-most competitors in cloud computing service platforms. So, if you are aiming for a cloud data engineer job, spend time learning about the fundamentals of cloud computing and work on projects that give you a hint of how to utilize at least one of the three platforms for real problems.

**Know-How of Data Warehousing and ETL Tools**
The previous section of this article precisely highlighted that data engineers are required to build efficient ETL/ELT pipelines. These data pipelines are fundamental to any organization that wants to source data organized and efficiently. And to achieve that, there are tools like Snowflake, Star, etc., for working on cloud data warehouses. Whether an aspiring data engineer or database administrator, data warehousing skills are essential to building a successful data engineering career.

**Big Data Skills**
We are living in the age of information, that too of the size of petabytes. And for handling such large datasets, the Hadoop ecosystem and related tools like Spark, PySpark, Hive, etc., are prevalent in the industry. So, as a data engineer who is required to interact with large datasets, having experience with such Big Data tools is a must. 

**New-Age Data Engineering Tools**
So far, we have discussed common data engineering skills, but recently, many new tools have come into use, for example, Snowflake for warehousing, dbt for ELT, Airflow for orchestration, etc. Make sure you always look for such tools and practice a few projects around them.

Apart from acquiring the essential skills, you can also sign up for any data engineer course that will help you better understand the fundamental data engineering concepts and make the best use of ProjectPro platform to work on real-world data science projects to master those skills.

**How to Become a Data Engineer**

Now that you have learned all about the skills and responsibilities of a data engineer role, you are likely to be curious about the steps to start learning data engineering. So, here are a few basic steps you must follow to start your career in the data engineering field.

The first step is to obtain a degree in a relevant discipline related to Big Data, such as computer science, software engineering, etc.

Focus on building skills specifically in computer science programming, data analysis, data modeling, machine learning, etc.

Complete a few relevant certifications for various big data and cloud computing tools.

Learn more about these tools by working on real-world problems.

Start applying for a few data engineering jobs to understand the industry demands and plan your path accordingly.

If you are willing to know how to become a data engineer without a degree, the below section will help you understand the steps you need to follow.

**How to Become a Data Engineer Without a Degree?**
Even without a degree, one can still work as a data engineer because there is no specific university degree for the profession.

Suppose you decide not to get a degree. In that case, you can still get certified as a software engineer through an online course and gain valuable experience as a developer. Becoming a skilled software engineer is the first step toward becoming a good data engineer.

Another option is to learn data engineering fundamentals if you don't have a degree. You should be familiar with the basics of computer science to explore the field of data engineering easily. To become a data engineer, one must have a solid understanding of programming languages and mathematics.

You should also look for volunteer work and internships since many organizations provide these alternatives and long- or short-term projects on data engineering to develop employees' skills. A data engineer's career can progress rapidly in the freelance and open-source markets. These places don't require professional degrees, only skills.

**FAQs**
1. _Is Data Engineering a Good Career?_
Yes, Data engineering is one of the hottest careers right now. It can be verified by the 2020 report from DICE, which revealed that Data Engineer emerged as the fastest-growing job in 2015 with a growth rate of 50% year-on-year.

2. _How can I start a career in data engineering?_
To start your career in data engineering, first, look at the roles and responsibilities of a data engineer and the skills required to become one. After that, focus on honing the skills and working on real-world data engineering projects.

3. _How long does it take to become a data engineer?_
It takes around four to six months to become a data engineer after pursuing a bachelor's or master's in data engineering. You need to work hard and stay focused on acquiring the right skills and industry-level expertise to launch your career in data engineering.

4. _Is it hard to become a data engineer?_
It is not hard to become a data engineer. Anyone can master the necessary skills to become a data engineer with hard work, time, and dedication.