Why Not to Become a Data Engineer
Tags: database, datascience, career, beginners
Published: 2021-11-29 14:16:24

As companies struggle to manage their massive and complex data sets, the necessity for data engineers has become more apparent.

Data engineering became the fastest-growing single job in 2019 with [50 percent year-on-year growth](https://seattledataguy.substack.com/p/are-companies-hiring-fewer-data-scientists), and there's little reason to believe demand for data engineers will slow soon. As with all careers, though, there are pros and cons to data engineering. Here's what you need to know about this up-and-coming job field and some of the reasons you may or may not want to pursue it.

## What Is a Data Engineer?

Data engineers move, remodel, and manage data sets from 10s if not 100s of internal company applications so analysts and data scientists don't need to spend their time constantly pulling data sets.

They may also create a core layer of data that lets different data sources connect to it to get more information or context. Data engineers spend their time developing data pipelines, managing data warehouses and maintaining all the various infrastructure components they develop along the way.

These specialists are usually the first people to handle data. They process the data so it's useful for everyone, not just the systems that store it.

There are obvious reasons to become a data engineer --- like a high salary and numerous opportunities due to limited competition within the job market --- but we're not focusing on those today. Instead, let's ask the question, why not to become a data engineer.


What Skills Does a Data Engineer Need?
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To assist you as a new data engineer, I have created a [skill set pyramid](https://www.youtube.com/watch?v=LgSHaOvNodA) that can be thought of as a hierarchy of skill set needs. This will help you focus on the skills you should learn first, allowing you to build a solid foundation as you move on to more specific skills. Just remember, the way you learn each step of the pyramid does not need to be overly rigid or stay in a strict order. You can layer each step, helping you progress as you learn. Let’s get started!


## Reasons Not to Become a Data Engineer

Despite being an in-demand career that promises high earnings and job security, becoming a data engineer isn't for everyone. As with most professions, it's important to consider your own skills, talents, and personality before choosing a career in data engineering. Here are some of the reasons you may not want to become a data engineer.

## You're Letting Money Drive Your Career Choice

While money is certainly important, it shouldn't be the driving force behind your career choice. Assuming you're planning to work in the tech field anyway, it's better to [choose a role you will genuinely enjoy](https://www.youtube.com/watch?v=MjuQIdSwa3w), even if the earnings could be a bit lower. A difference of $5,000 or even $10,000 in earnings won't drastically impact the lifestyle of a highly paid tech worker, especially once taxes are taken into account. The level of enjoyment you derive from your work, on the other hand, will affect your overall happiness and satisfaction in your professional life.

The average data engineer earns [$92,650 per year](https://www.payscale.com/research/US/Job=Data_Engineer/Salary), which is significantly above the [overall US average of $53,490](https://www.jobted.com/salary). The financial benefits of becoming a data engineer, however, become much less clear when compared to other jobs in the tech field. The average software engineer, for example, can expect to make [about $87,690](https://www.payscale.com/research/US/Job=Software_Engineer/Salary). As you can see, the difference between a software engineer's salary and a data engineer's salary is fairly negligible. If software engineering would be a more fulfilling job for you, the slightly higher average salary isn't worth going into data engineering.

## You Don't Enjoy the Engineering Mindset

Data engineering requires you to adopt and deploy an engineering mindset, which some people can find rather constrictive. Because data engineers often need to create pieces of infrastructure that other engineers can maintain in the future, they must [work within a strict set of rules and standards](https://www.youtube.com/watch?v=qJWZbZ5NNJ8&t=102s). These rules are extremely important but can also seem burdensome to those who prefer more creative freedom in their projects.

This isn't to say, of course, that there aren't creative aspects to the engineering mindset. High-level problem solving, for instance, often requires engineers to develop creative solutions. Likewise, engineers use creative problem-solving skills to [continuously improve](https://www.engineeringpassion.com/developing-the-engineering-mindset/) their projects. In order to be a successful data engineer, you'll need to be able to balance your creative impulses with the rigorous mindset of an engineering professional.

## You Aren't Flexible Enough

One of the most interesting aspects of being a data engineer is the flexibility and lack of definition for the role. Because of its [highly interdisciplinary nature](https://www.youtube.com/watch?v=qJWZbZ5NNJ8&t=300s), data engineering combines elements of data analysis, programming, modeling, machine learning, and many other specific skills. Becoming a professional data engineer requires you to flexibly adapt and deploy these various skills as needed for the specific project you're working on.

The downside of this interdisciplinary approach to data engineering is that it requires more flexibility than most other tech jobs. In your data engineering career, you may take on drastically different roles at different companies while maintaining the same job title. If you prefer to have a well-defined, set role, you likely won't enjoy the somewhat chaotic world of data engineering.

## You Don't Enjoy Continuous Learning

A final reason you may not want to become a data engineer is that you don't enjoy the process of continuous learning. Technologies are constantly shifting and evolving, requiring data engineers to update their skills on an ongoing basis. The[ cloud data warehouse tool Snowflake](https://www.theseattledataguy.com/what-is-snowflakedb-and-why-you-should-use-it-for-your-cloud-data-warehouse/#page-content), for example, has seen [substantial growth over the last 10 years](https://www.morningstar.com/articles/1020679/snowflake-shows-substantial-growth) as companies have embraced cloud computing. As trends like this emerge, data scientists must learn to use new tools and technologies to stay at the cutting edge.

If one of your career goals is to eventually stop learning and rest on your laurels, data engineering won't be a good fit for you. Indeed, this is [true of almost all roles](https://www.youtube.com/watch?v=qJWZbZ5NNJ8&t=448s) in the tech industry. Continuous learning is crucial for staying on top of trends and technologies, and even the most seasoned experts must pursue ongoing education to remain relevant. Failure to stay on top of new developments practically guarantees that your skills will eventually become outdated. While you might be able to work with older technologies at a handful of companies, your career options will narrow significantly when you stop updating your skills.

## Conclusion: Know Yourself

As you can see, knowing yourself and your preferences is essential to deciding whether a career in data engineering is right for you. In addition to knowing what you like to do in terms of specific tasks and working conditions, it's also important to consider your own personality. Data engineering is a suitable role for people who prefer to [work in the background](https://www.youtube.com/watch?v=qJWZbZ5NNJ8&t=639s) within a company, rather than directly driving conversations with management using data insights. If you prefer that more extroverted side of data, though, you may enjoy a role as a data scientist.

Overall, becoming a data engineer is a great career choice for people who love detail, following engineering guidelines, and building pipelines that allow raw data to be turned into actionable insights. As mentioned earlier, a career in data engineering also offers excellent earning potential and strong job security. With that said, the job isn't for everyone. If some of the reasons detailed above seem to describe you, it may be a good idea to give data engineering a second thought and explore other tech careers that could fit you better.

If you want to read/watch more about data engineering, then check out the links below:

[Data Engineer Vs Analytics Engineer Vs Analyst](https://www.youtube.com/watch?v=uEPCxBaRf6A)

[Why Migrate To The Modern Data Stack And Where To Start](https://www.theseattledataguy.com/why-migrate-to-the-modern-data-stack-and-where-to-start/)

[5 Great Data Engineering Tools For 2021 -- My Favorite Data Engineering Tools](http://youtube.com/watch?v=s40MptE20Tc&t=1s)

[4 SQL Tips For Data Scientists](https://www.youtube.com/watch?v=kSt9NV-qZkc&t=1s)

[What Are The Benefits Of Cloud Data Warehousing And Why You Should Migrate](https://logitanalytics.com/what-are-the-benefits-cloud-data-warehousing-and-why-you-should-migrate/)