So you want to become a big data engineer? Good choice! But how to go about it? Find out in this post.
In 2012, Harvard Business Review announced that data scientist was officially the ‘sexiest job of the 21st century.’ Today, this is changing. In the weird, wonderful, and fast-evolving world of data, the specialized role of big data engineer is fast becoming indispensable. Data engineers don’t get as much hype as data analysts and data scientists, but without them, data would remain an inscrutable mass of meaningless information.
In fact, according to a 2019 report commissioned by the Royal Society (the UK’s national academy of sciences) data engineering is becoming the UK’s most in-demand data-related role. International trends are following suit. So, a career in data is an excellent move—and big data engineering is just one exciting path you might choose to follow.
In this post, we’ll explore how to become a big data engineer. We’ll cover:
- What does a big data engineer do?
- Why become a big data engineer?
- How much do big data engineers earn?
- What is the typical background of a big data engineer?
- How to become a big data engineer
- Wrap-up and further reading
Ready? Then let’s learn.
1. What does a big data engineer do?
OK, first things first. What does a data engineer actually do?
In short, a big data engineer develops, manages, and maintains an organization’s data infrastructure. Their job involves collecting and transforming data and storing it in databases in a format that’s easier for others to use. They generally work with senior leadership, data analysts, and data scientists. This helps ensure their models and systems are closely aligned with the needs of individual teams, as well as with the wider business strategy. With all this responsibility, data engineers tend to work in rather senior roles.
To help put the role of the data engineer in context, you might also want to check out the role of the data analyst, as well as the difference between a data engineer and a data scientist.
What tasks does a big data engineer carry out?
A big data engineer’s tasks all revolve around making sense of vast, unstructured datasets. You can learn more about the difference between structured and unstructured data (and why it matters) in this guide. Ultimately, data engineers must bring datasets into some sense of order. A competent data engineer, therefore, is just another type of data scientist. However, rather than focusing on broader strategic questions, their focus is on creating structure and carefully designing bespoke systems. This can involve developing data pipelines (for funneling data into data lakes or warehouses). It can mean developing specialist data analytics algorithms for other areas of the business. And it most certainly involves becoming an expert at using a wide range of different big data technologies and programming languages.
This should give you a broad flavor but you can learn more about what a big data engineer does here.
2. Why become a big data engineer?
Everyone’s always saying how great data analytics or data science can be, but is big data engineering a rewarding career path, too? The short answer is a resounding yes. But with a caveat—it’s challenging! Overcoming these challenges is part of the reward, however, as we’ll see. Here are a few reasons why big data engineering can be a great career choice.
An opportunity (and motivation!) to perfect your programming skills
Building data pipelines; managing data system security; creating scalable databases, or simply auditing data—all these vital tasks involve excellent programming skills. Since many data engineers come from a software engineering background, this is one challenge that should certainly appeal!
Flexibility to choose your path
In all honesty, becoming a data engineer can be hard. But once you’ve nailed the key skills and landed your first job, you’ll find plenty of freedom to develop your dream role. You’ll get to choose what you’re working on and when, and will rarely be told what tools to use. This keeps things fresh while offering myriad opportunities to develop new software skills.
Because data engineers require a grounding in data management and software development, there’s also a much wider scope for the sort of output you produce. Maybe you want to work as part of a team of engineers for a big data-driven business? Or perhaps you’d rather be the lone ranger expert for a tech start-up? The choice is yours. The options are broad and no two roles will ever be the same.
You’ll develop a broader business and analytical focus
Most often, software developers create products for customers or internal users. This can quickly descend into responding to bug reports, resolving software issues, and so on. As any developer will tell you, this can feel a bit like fire fighting! However, once you bring data into the equation, everything changes.
With data skills under your belt, you can develop new products and analyze their performance in real-time. And using data to measure impact and drive effective change is exactly what makes data engineers so valuable. As you shift away from hardware support towards strategic input, you’ll become increasingly involved in making decisions that impact the business. This can feel like a lot of responsibility but it’s also highly rewarding.
Ideal for remote working
While there’s been a boom in remote working since the coronavirus pandemic, not all jobs are ideally suited to working from home. Luckily, that’s not the case for big data engineering. So long as you have the right hardware, security, and a stable internet connection, most data engineering tasks can be carried out remotely.
As an in-demand role, you’ll also find organizations are generally more willing to accommodate your needs—leverage that! And, of course, if you want to work as a contractor, working remotely is practically a requirement. Bonus!
Contribute to open-source projects
While you may find yourself working for a private organization, that doesn’t mean your sphere of influence stops there. Many of the tools that software engineers use are open source (meaning that their code is freely available to develop and share).
According to the 2019 Stack Overflow Developer Survey, approximately 65% of software engineers contribute to open source projects once a year or more. Throwing data engineering skills into the mix adds even greater value to open source projects. With your specialist skills, you can make a real-world difference in the community that goes far beyond the organization you work for.
On top of these reasons to get into data engineering, there’s one more we haven’t yet covered, which you may be wondering about: Salary.
3. How much do big data engineers earn?
OK, let’s get down to the details: what does a big data engineer’s salary look like? With a background in areas like software development, data management, and academia, most data engineers already have a solid career foundation to build on. So it’s a safe bet to say they can earn a pretty decent salary as a data engineer, too.
To get a gauge, we took an average of estimates from the job and salary comparison sites Glassdoor, Payscale, Salary Expert, and salary.com. Crunching the numbers tells us that the average big data engineer in the United States can earn about $108K per year. This figure isn’t set in stone—the real amount depends on factors like work experience, specialist skills, location, and the company you’re working for. But it gives you a pretty good idea.
To dig into more detail on this, you’ll find a comprehensive big data engineer salary guide here.
4. What is the typical background of a big data engineer?
Until recently, you wouldn’t find many people who started their career thinking: “You know what? I’m going to become a data engineer!”
Roles for professionals in the field of data have typically emerged as the need has arisen. For instance, many of those entering data engineering today started as software developers who required some data and engineering skills to do their job better. Another example is big data enthusiasts who get a buzz out of bringing order to all that chaos. By simply playing around, many realized that they had a skill and that there was a demand for these skills.
While there’s no single route into the field, most engineers indeed start their careers in a select number of other fields. These fields nurture the specific skillsets which lend themselves well to big data engineering. The most common backgrounds for data engineers include:
- Other data-driven roles, e.g. data analytics or data science
- Software engineering, e.g. API development or full-stack development
- Big data consultancy
- IT or system administrators
- Academia, e.g. the Ph.D. route
If you don’t have a background in any of these areas, don’t worry. It’s no longer the deal-breaker it once might have been. While data engineering is a relatively new profession, the idea that you can only ‘grow into it’ is starting to change.
The emergence of online courses and even college degrees that focus specifically on data engineering means that the field is becoming accessible to an increasingly diverse group of people. Keep your mind open to all the possibilities!
5. How to become a big data engineer
By now, you should already have an idea of what’s involved in becoming a big data engineer. But if you’re determined to go down this route and don’t want to leave it up to chance, here are some useful steps to help you along the journey.
1. Get a degree
Even entry-level data engineers require some kind of degree, usually in a field like computer science, software engineering, physics, or applied math. Ideally, this won’t just provide you with a solid grounding in the principles of software development and/or data—it should cultivate the ‘soft’ skills you need to thrive in the role, too. Soft skills include things like communication, team working, and problem-solving.
An emerging option at many colleges is that of degree apprenticeships—an excellent alternative to a traditional degree. A degree apprenticeship combines academic training with practical work experience with an employer, allowing you to pick up real-world experience as you learn. This is an increasingly popular option, it’s cheaper than a full-time degree, and it has a high level of success—most people land their first job as soon as they graduate.
2. Consider taking a certified course
Perhaps you’ve already established yourself in another field, or already have a degree? If so, another option is to take a certified online course in an area like data analytics. Compared to a full degree, this is a targeted, fast, and relatively cost-effective way of ‘topping up’ your relevant skillset.
Depending on the course you choose, you can focus on key areas like big data architecture, machine learning, or data analytics. A comprehensive and well-designed course will also teach you the basic tools you need to become a data engineer (for instance, Python and SQL). This is a great option if you don’t want to go down the college route (which, let’s face it, is pretty pricey). We’ve rounded up the highest quality data analytics certification programs here.
3. Get real-world experience
Even with a qualification, the truth is that data engineering isn’t really a ‘first job out of college’ kind of role. As data engineering solidifies into a specialist discipline, this is gradually starting to change. But for the time being, you’ll still need some relevant real-world experience.
Most entry-level data engineering jobs call for experience, but what they mean by ‘experience’ is often pretty broad, which is a good thing. It could be that you’ve worked in a previous role with data (perhaps as an analyst or in data science), in software development, or as an intern in a related field. Keep your options open. Even creating a portfolio of sample projects is a great start.
4. Get to know your databases
Make sure you’re up to date on both your general knowledge of databases and the tools you might use to manage them. Databases are a fundamental part of data engineering as they are the building blocks of larger infrastructures.
At the very least, familiarize yourself with things like SQL (Structured Query Language), as well as NoSQL frameworks. It’s also a good idea to play around with a database management system like MySQL or PostgreSQL. We especially like these two because they’re both open-source, but there are plenty of commercial solutions also available.
5. Develop your broader toolset
Developing your knowledge and skills using different web-based data engineering tools will vastly improve your job prospects. There are tonnes out there to choose from but a few to consider include Amazon Web Service’s cloud architecture, the Apache Cloudstack, and Microsoft’s SQL Server Management Studio.
While nobody will expect you to become an expert in all of these tools, it’s important to familiarize yourself with them at the basic level. With such a huge array of data engineering tools out there, it’s pretty common to find them integrated. You’ll need to understand their basic principles, then, if not their inner workings.
6. Embrace alternative job opportunities
The pathway into big data engineering is often a long road. If you don’t find your dream job right away, don’t be disheartened. It’s better to consider big data engineering as a long-term objective, especially if you’re inexperienced. In the meantime, many related jobs—even those that aren’t directly related to data—offer a great learning curve.
For instance, you’ll only benefit from spending some time working as a developer; a role that may be easier to land. Likewise, becoming a data analyst can be a great stepping stone towards data engineering. Any computer- or data-related role will teach you important skills. And in the end, the most effective data engineers are those with a breadth of expertise gathered from a colorful array of different jobs and at different levels. Don’t give up!
6. Wrap-up and further reading
In this post, we’ve introduced the concept of data engineering and explored everything you need to know to start your career as a big data engineer. As we’ve covered, the role can be challenging but equally rewarding. While data engineering requires dedication and long-term thinking, there are lots of routes you can take and practically endless possibilities for growth.
To learn more about data analytics try this free, five-day data analytics short course. You might also be interested in the following posts: