Your Step-by-Step Guide to Become a Data Engineer in 2024

Learn everything about data engineering, what are data engineers? What do data engineers do? How to become a data engineer? All at one place in this blog.

Your Step-by-Step Guide to Become a Data Engineer in 2024
 |  BY Manika

If you are planning to make a career transition into data engineering and want to know how to become a data engineer, this is the perfect place to begin your journey. Beginners will especially find it helpful if they want to know how to become a data engineer from scratch.

In 2018, the Wall Street Journal reported that every company is a tech company, suggesting that every company is likely to hire a tech co-founder for future growth. When one discusses tech, it is unlikely they will miss out on the opportunity to discuss the power of data. Clive Humby, the renowned mathematician and an entrepreneur in the data science space, rightly highlighted the importance of data with his quote, “Data is the new oil.” The International Data Corporation has suggested we accumulate 180 zettabytes of data in 2025. 

The important question is, how will companies handle and leverage that data?  And, if you think the answer is by hiring more data scientists, then we’d like to burst your bubble by stating that the answer is by hiring many data engineers. The primary reason for that is the need to have more frontline workers who can retrieve data from various data sources. Even as per Michelle Goetz of Forrester research, “There may be twelve times as many unfilled data engineering jobs as Data Science jobs.”.

Without any further ado, let us explore what exactly makes this job role so unique by discussing what a data engineer is, their responsibilities, skills required, job demands, how to become a data engineer, etc., in the following few sections.


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What is a Data Engineer?

how to become a data engineer

Imagine you are planning to start a small convenience store. The first thing you're likely to do is to prepare which items will be available in your store and how you will source them. Similarly, companies with vast reserves of datasets and planning to leverage them must figure out how they will retrieve that data from the reserves. 

A data engineer a technical job role that falls under the umbrella of jobs related to big data. The job of data engineers typically is to bring in raw data from different sources and process it for enterprise-grade applications. We will look at the specific roles and responsibilities of a data engineer in more detail but first, let us understand the demand for such jobs in the industries.

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Data Engineer Jobs- The Demand

Data Engineer Jobs Demand

Data Scientist was declared the sexiest job of the 21st century about ten years ago. While many believe that the hype around this job still exists, the data suggests otherwise. As per the Data Science Interview Report by interviewquery, interviews for data scientist jobs grew by only 10%, and the number of interviews for data engineering roles increased by 40% in 2020. In the same year, Glassdoor removed data scientists' jobs from the top position for the first time since 2016.

Additionally, the website reported that the number of job positions was almost similar in 2019 and 2020. The demand for other data-related jobs like data engineers, business analysts, machine learning engineers, and data analysts is rising to cover up for this plateau. 

Recommended Reading: Data Engineer vs. Data Scientist- The Differences You Must Know

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 (image below).

Data Engineer Role

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?

What does a Data Engineer do

A data engineer is at the forefront of employees interacting with a company’s most important resource: data. Their primary responsibility is to ensure that different company teams can smoothly analyze the data and use it for various purposes. Data engineers work to source data using ETL pipelines and make it more readable for the whole organization. Along with this, there are many other tasks that data engineers typically perform. 

Go through the section below to know the complete list of responsibilities of data engineers.

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Role and Responsibilities of a Data Engineer

  • Prepare, handle, and supervise efficient data pipeline architectures.

  • Build and deploy ETL/ELT data pipelines that can begin with data ingestion and complete various data-related tasks.

  • Handle and source data from different sources according to business requirements.

  • Work in teams to create algorithms for data storage, data collection, data accessibility, data quality checks, and, preferably, data analytics.

  • Connect with data scientists and create the infrastructure required to identify, design, and deploy internal process improvements.

  • Access various data resources with the help of tools like SQL and Big Data technologies for building efficient ETL data pipelines.

  • Experience with tools like Snowflake is considered a bonus.

  • Build solutions highlighting data quality, operational efficiency, and other feature describing data.

  • Create scripts and solutions to transfer data across different spaces.

Data Engineer Salary

Data Engineer Salary

Since the demand for data engineers is continuously rising, the salary expectations for the role also seem to be higher. Data engineering is a lucrative field, which is one of the many reasons you should pursue a career in it. Let us look at data engineers' average salaries in some major countries worldwide.

  • The average annual salary for a data engineer in the US is around $115,157, which is quite higher than that of a Data Scientist ($101,995) or a Software Engineer ($93,965).

  • The annual average salary of a data engineer in India is ₹10,70,746.

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

  • In Australia, a data engineer has average yearly compensation of A$1,10,000.

  • Data engineers in Germany earn an average income of €64,702 per year.

  • In Russia, a Data Engineer can expect to earn 2,24,492 PP per year on average.

After reading about the above data engineer job description and getting an idea of how lucrative the role is, one is likely to be interested in knowing what skills are required to pursue the path of data engineering. The same is discussed in the next section.

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Data Engineer Skills

Skills Required to Become a Data Engineer

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.

  1. 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.

  1. 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.

Project Idea: SQL Project for Data Analysis using Oracle Database

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  1. 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. 

Project Idea: Build Regression (Linear, Ridge, Lasso) Models in NumPy Python

  1. 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.

Project Ideas: AWS MLOps Project to Deploy Multiple Linear Regression Model

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Azure Deep Learning-Deploy RNN CNN models for TimeSeries 

Learning Resources: How to Become a GCP Data Engineer 

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  1. 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.

Project Idea: Snowflake Real-Time Data Warehouse Project for Beginners

  1. 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. 

Project Idea: Hands-On Real-Time PySpark Project for Beginners

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  1. 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.

Recommended Reading: How to Become a Big Data Engineer in 2022?

Data Engineer Certification

Data Engineer Certification

Professional certifications can help job seekers who want to work as data engineers launch or accelerate their careers and offer them an additional advantage over their competitors. These certifications assess a person's knowledge and abilities against industry benchmarks to show hiring managers that the candidate has the skills to be competent and participate in creating and implementing corporate data strategy.

Here are a few valuable data engineer certifications to pursue and upgrade your data engineering skills.

Data engineers are in charge of gathering, transforming, and distributing data. You have a wonderful opportunity to enhance your skills by earning the Google Professional Data Engineer certification, which verifies your data engineering expertise. You will develop data processing systems, supervise solution QAs, and create ML-powered data processing models as part of the course certification. You will also learn how to orchestrate Google's data platform tools to improve end-to-end governance, compliance, and security protocols.

Anyone intending to build job-ready skills, tools, and a portfolio for an entry-level data engineer should pursue this professional certificate. You will perform just like a data engineer during the self-paced online courses and learn the fundamental skills needed to work with various tools and relational databases to develop, deploy, and manage organized and unstructured data.

You will be able to identify and perform the main responsibilities of a data engineering role after completing this Professional Certificate. You will use Python programming and Linux/UNIX shell scripts to extract, transform, and load (ETL) data. You will use SQL statements to query data in Relational Database Management Systems (RDBMS). You will work with unstructured data and NoSQL relational databases. You will learn about big data and work with tools like Spark and Hadoop. You will gain experience building data warehouses and using business intelligence technologies to analyze data and derive insights.

During this five-month curriculum, you will learn how to create data models, data warehouses, and lakes, work with huge datasets and automate data pipelines. You will understand how to build relational and NoSQL data models to match the various demands of data consumers. You will create PostgreSQL and Apache Cassandra databases using ETL. This program will enhance your data infrastructure knowledge and data warehousing skills.

Additionally, using Amazon Web Services, you will build a cloud-based data warehouse. You will keep big data in a data lake and use Spark to perform queries on it. Use Apache Airflow to schedule, automate, and monitor the data pipelines. In addition, you will deal with production data pipelines, execute data quality checks, and verify data history. 

The best way to use the new skills you have learned throughout any of these programs to build your own data engineering capstone projects from ProjectPro.

How to Become a Data Engineer?

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.

One of the best approaches for learning something new is experiential learning, the practice of learning by doing. And to learn data engineering through that approach, we suggest you work on practical enterprise-grade projects in data engineering. Working on such projects will give you hands-on experience of the entire end-to-end data engineering project lifecycle. So, check out ProjectPro’s repository to hone the right skills for pursuing data engineering.

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.

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.

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.

 

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About the Author

Manika

Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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