Is Data Engineering Easy? Or, Is It Hard?

As more and more people are considering data engineering as their careers, there are a few basic questions that pop up frequently. The first question that comes to mind of any aspiring data engineer is if it’s easy to land a job in this field. Although ‘easy’ and ‘hard’ are subjective, you can get a sense of what it’s like by reading about the skills required for becoming a data engineer.

Data engineering is hard. It’s a highly technical and challenging profession. However, with patience and dedication, anyone can learn the skills required to become one. Experience is more valuable than education, so it’s best to learn the basics, land an entry-level job, and start growing.

In this article, we’ll discuss everything you need to know about becoming a data engineer. And we’ll also look at how difficult or easy it can be depending on your background.

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

Is Big Data Engineering Hard?

Data engineering is a broad discipline, and the specifics of the position depend on the company you work for. In some organizations, data engineers perform every step of the data process, including collection, filtering, storage, interpretation, and analysis of data.

However, a typical data engineer only concerns themselves with providing organized, consistent data flow to help data scientists and machine learning engineers do their job. Data engineers build robust data pipelines to process large amounts of data coming from various sources.

As you can see, data engineering is a highly technical job that focuses on moving, transforming, and storing data. You’re required to master several tools and technologies. So you should expect to be challenged while learning the ins and outs of data engineering.

A computer science background or software engineering background will help you understand data engineering better. And there are valuable paid and free resources on the internet that make things easier. Proper guidance and study material go a long way in making it easier for you to become a data engineer.

But this doesn’t mean you won’t have to work hard to acquire the skills. So to answer the question, data engineering is not easy—and it shouldn’t be! If data engineering was a piece of cake, there wouldn’t be a shortage of talented data engineers.

With patience and perseverance, anyone can learn the necessary skills and become a data engineer. Just be prepared to invest a decent amount of time and effort.

Do You Need To Get a Data Engineering Certificate?

So now you know data engineering required effort on your part. And when you search online for data engineering, you’ll find various online institutions offering comprehensive courses with certifications. But how necessary are those certificates? Do they help you land a job, or are they just a piece of paper?

The good news is that data engineering certificates don’t really matter. Since data science is a new industry, there are no established academic requirements for data scientists, data engineers, or data analysts. When an employer reads your resume, they aren’t looking for a specific name; they want to see proof of skills.

Learning certificates only show that you’ve completed a particular course. They don’t prove that you have the skills to handle data engineering tasks. Relevant work experience is much more valuable than certifications. If you lack professional experience, i.e., you’re a fresher, you must include relevant projects.

Building personal projects is an excellent way to showcase your skills to employers. It’s crucial to have an impressive GitHub portfolio. Don’t just keep learning; build lots of challenging personal projects requiring effort on your part. Most data engineering courses include several tasks for you to practice. And a quick google search will also give you some ideas to get started. 

How Long Does It Take To Learn Data Engineering?

There’s no definite answer to this question; it depends entirely on how consistent and dedicated you are to learning data engineering. Your experience level and schedule also play a role in determining the time to completion.

For example, it’ll take you less time to grasp the concepts if you have a computer science background. And things will be much easier for you if you’re currently a software engineer wanting to shift to data engineering.

Studying online has the added benefit of being able to do it whenever you want. But you also run the risk of procrastinating and being irregular with your studies. So it’s entirely up to you how long you take to learn data engineering.

However, most people seem to learn the essentials and build a few projects in less than a year. So if you start learning today, you will probably get a job in the next 12-15 months.

It must be pointed out that you don’t need to know everything to get your first job. For example, very few people know Python inside out—and you’re not expected to know it either. It’s enough to learn how to use Python in the context of the data-related problems you’re trying to solve. 

When it comes to data engineering, experience is more important than education. You’ll learn a lot during your first job, and it’ll form a base for you to enhance your skills and grow your career.

Is Data Engineering Harder Than Data Science?

Data engineering is often confused with data science. And those who know that they’re different compare each other in terms of salary, difficulty, and career growth. However, the truth is that they are two distinct positions with different responsibilities. Though there are a few overlapping skills, they are not interchangeable.

Data engineers gather data from different sources, clean it, and store it in the best formats. They build data pipelines to ensure consistent data is delivered to data scientists and data analysts. On the other hand, data scientists work to understand the data generated by data engineers. They visualize the information and extract patterns to get new insights.

As you might have guessed, it depends on the person which position they prefer. And the question of difficulty shouldn’t arise when deciding which side you’re on. This is because you want to do what you enjoy doing instead of doing what’s “easier.” Spoiler: you’ll find that learning something you’re uninterested in is always more difficult.

What Skills Are Required?

The first requisite for data engineering is Python. R and Java are alternatives, but Python is the standard language for all things data. Next, you need to know how to handle databases. SQL is the standard language for this, though there are also NoSQL databases.

Apart from these programming languages, you have to learn ETL (Extract, Transform, Load) tools, data APIs, data warehousing solutions, and distributed systems. Basic knowledge of machine learning algorithms and data structures also helps data engineers understand the work and needs of data scientists better. Data engineers also use services like Azure, Amazon Web S3, Apache Spark, and Apache Hadoop.

Conclusion

Data engineering is an essential part of the data process, and it’s the backbone of data science. And aspiring data engineers often wonder how difficult it is to get a job as a data engineer.

Since data engineering is a highly technical position, it is difficult to learn and master. However, plenty of useful resources are available online to help you understand the essentials of data engineering while building fun projects on the way.

With consistency and dedication, it should only take you around 12 to 15 months to land a job as an entry-level data engineer.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

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