The need for data engineers is skyrocketing as companies are looking for individuals capable of acquiring, handling, and storing their data. However, there’s also a lot of confusion among job seekers regarding a data engineer’s responsibilities. When you read the job description, it may sound dull and perhaps repetitive, but it’s not so.
For the most part, data engineering is not boring. A typical data engineering job can have many technical challenges, making it an exciting career for those who love to solve problems. However, depending on the organization, you might end up building the same data pipelines over and over again.
This article will discuss the job responsibilities of a data engineer, including both the challenging and the potentially boring sides of data engineering. Then, we’ll conclude with some considerations to help you decide if this job is for you.
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!
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The Job of a Data Engineer
There’s a lot of confusion about what exactly data engineers do every day on the job. This is because their job responsibilities vary greatly depending on the organization they’re working in. Most data science jobs are this way, asking you to adapt according to the company’s requirements. Data engineering is a broad term, and it can mean different things in different companies.
In some organizations, data engineers only collect and safely store data using data pipelines. They work alongside data scientists and data analysts and help them make use of the data they gather.
Essentially, these data engineers build robust data pipelines that clean and transform unorganized data into databases. They compile database systems, write complex code, and install recovery systems.
On the contrary, some professionals perform many tasks of data analysts as well, all under the “data engineer” label. This typically happens in small teams and startups where there aren’t many ‘data-focused’ employees.
These professionals often perform and supervise every step of the data process. They manage, store, clean, and arrange data like every other data engineer. However, they may also analyze the data and apply machine learning to make sense of it.
Then, there are places where data flow management is a big part of the process. Here, data engineers manage database warehouses and develop table schemas. They mainly use languages like SQL and ETL techniques and concepts.
Even though their role varies from company to company, data engineers typically work with databases, pipelines, and big data tools. Occasionally, they may also use visualization and machine learning techniques, which are used by data analysts and data scientists, respectively.
The Fun and Challenging Side of a Career in Data Engineering
Let’s get to the fun part of the article. Data engineering has a lot to offer, especially in terms of technical challenges. There are many technologies, concepts, and methods you can use to solve various data-related problems.
As a data engineer, you have to think of creative uses of data. Protecting data quality, handling complex datasets, and managing high-value data is part of the day-to-day activities of a data engineer. You sometimes also write software that people in the organization use to make sense of the data.
System administrators, other data engineers, data scientists, and data analysts are always close to you. You need to have a broader grasp on all things data, from setting up a server to coding models for data scientists.
Data engineers are the backbone of data processing. Data scientists and data analysts are able to do their jobs only because data engineers spend hours behind the desk compiling raw data. However, data engineers don’t just make the lives of data scientists easier; they also make a significant impact on the world.
It is estimated that 2.5 quintillion bytes of data are created every day by humans (FYI, there are 18 zeros in a quintillion). By 2025, this number is expected to rise to 463 exabytes (one exabyte is equal to… one quintillion—different names for the same thing).
This growth in technology means a surge in the amount of data and the number of sources. Therefore, data engineers are indispensable for effectively processing and channeling this big data.
And this also means that data engineers can choose from a variety of ways to pursue their passion and enhance their skills. There are dozens of tools and techniques data engineers can use to work. You don’t have to know everything in the world, but once you have the skills to land jobs, you have total freedom to choose what work to do and what tools to use.
Potentially Boring and Soul-Sapping Aspect of Data Engineering
Data engineering is not always fun. You may find yourself in a role where you only have to create data pipelines for others. Creating the 112th version of the same data pipeline is tedious and unrewarding.
However, the thing is, data engineering is just that: data engineering. You cannot make a blanket statement like “data engineering is fun” or “data engineering is boring.” It entirely depends on the company you’re working for. Let’s look at a few examples to understand the point.
Nvidia Corporation aims to build image analysis applications to improve productivity in agriculture and healthcare. How would you feel if you were a part of that team as a data engineer? You would probably find it interesting since you would be making a positive impact on the world.
And what about working at Facebook? How would you feel about helping the company track its users’ phones all the time to serve them targeted ads? The job might be mentally stimulating because of the large amounts of data. But you would probably find it soul-sucking because you’re essentially helping them invade people’s privacy.
So it depends entirely on what you’re working for. Are you working to make the world a better place, or are you only making the rich few even richer? Even if your job is repetitive, you will find ways to get creative if you think the work is meaningful. In contrast, you’re bound to feel unmotivated and bored even though you may be solving complex data problems.
Finally, the most crucial question you need to answer is whether data engineering suits your personality. Do you like working alone, or do you prefer human interaction? Data engineering can be a lonely job at times, especially in smaller companies.
Also, data engineering doesn’t require as much mathematics as data science. So your liking for math may also help you decide if this job is for you.
Of course, money is also an important consideration. In some cases, how much paper you take home is more important than if the job is fulling or not. You may have to agree to work in a dull job for several years if you have a big mortgage to pay.
Bottom line: data engineering is neither fun nor boring as such. It depends on the company you’re working for. If you can find meaning in your job, even relatively boring work can be fulfilling. On the contrary, you may find challenging work at huge firms to be soul-sapping.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Data engineering is on the rise, thanks to the advancements in technology. It pays well over $100k a year, making it an attractive career choice for youngsters.
It is often labeled as boring or soul-sucking, which is not at all true. Data engineering can be full of exciting challenges. There are many tools and technologies to learn and choose from for different projects and problems.
However, what’s more important is finding meaning in your job. If you work for a company that aims to make the world better, you’ll enjoy it regardless of your position’s responsibilities.
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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