The four key things to know about the difference between a data engineer and a software engineer are:
- Data Engineers and Software Engineers earn a comparable salary and hold similar knowledge, but take different approaches in the workplace.
- Data Engineers are more micro-focused while Software Engineers look at things from a macro perspective.
- The Data Engineer role is ideal for individuals that want to gain experience with machine learning, big data, and building data pipelines.
- Individuals who hold the title of Software Engineer are capable of wearing many hats, including that of a backend engineer, build engineer, database engineer, full-stack engineer, and so on.
When it comes to the world of technology, there are many roles that share similar responsibilities, from data scientists to data architects. On paper, it's easy to become confused by all the seemingly minor differences between such titles.
The title of data engineer vs. software engineer is a particularly good example, as there are a number of areas where they overlap. To help you understand here's a more detailed comparison of these two roles and their potential importance within your organization.
Table of Contents:
- The Role of a Data Engineer
- The Role of a Software Engineer
- Data Engineer vs. Software Engineer
- Who Should You Hire?
- Arm Your Team with the Right Tools
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The Role of a Data Engineer
To properly compare a data engineer vs. software engineer, you have to first understand the responsibilities of each.
The primary goal of a data engineer is to set up and maintain data infrastructure. The systems they work with handle core information and applications of a business, and they can range from small relational databases for local businesses to petabyte-scale systems for major companies.
To fulfill their extensive role, data engineers take on a number of responsibilities, including designing, building, and implementing data systems to guide AI and machine learning analytics. In addition, data engineers may develop processes for acquiring, transforming, migrating, verifying, modeling, and mining data.
When it comes to what a data engineer needs to know in order to do all of these things, they typically understand a number of different programming languages, including Java, Python, and R. They're also intimately familiar with SQL and NoSQL databases and can use distributed systems like Hadoop.
All of this knowledge enables them to work well with data administrators, architects, and scientists. They can also perform the responsibilities that fall under these other roles, enabling them to work single-handedly to build robust and integrated data infrastructure.
If this all sounds like a lot, you'd be right. But data engineers get compensated for their knowledge and hard work to the tune of about $117,000 to $160,000 annually.
The Role of a Software Engineer
As we continue comparing a data engineer vs. software engineer, let's look more closely at the role of a software engineer.
Just as data engineers work with data, software engineers that specialize in data (also known as Software Data Engineers) work on data pipelines as well. Other common titles for these software engineers include Platform Engineer or Infrastructure Engineer, but their responsibilities remain the same.
These software engineers also need to know SQL, working with similar technologies as a data engineer, including Hadoop, AWS, and Spark. These software engineers also need to know data modeling. So, what separates them from data engineers? The primary difference is that software engineers take a more macro approach while data engineers are more micro-focused.
Software engineers must build out the scheduler, cluster manager, distributed cluster system, and similar infrastructure. They also have to focus on implementing the code that makes these systems function more efficiently. Beyond that, software engineers also need to be stronger programmers than data engineers.
They're going to work with similar languages as data engineers, including Python, but they also need to know Scala, Java, and Golang. Additionally, they'll need to work with a number of DevOps tools, such as Docker, Kubernetes, or a CI/CD tool such as Jenkins. The reason why these skills are so critical to a software engineer is that they are continuously testing and deploying services in order to make business systems work faster and better.
Given the breadth of their work, these software engineers are typically compensated about the same as data engineers. However, major companies like Apply may pay them over $159,000 annually and senior software engineers can make substantially more.
Data Engineer vs. Software Engineer
If your business is looking to build a Data Science and Machine Learning team, you ultimately will need to employ software engineers and a number of other roles that may seem similar on paper, but actually differ quite drastically.
Here's a direct comparison rundown of what you need to know about data engineers vs. software engineers.
They're Complementary, Not Interchangeable
Although these roles may share similar knowledge, what they do with that knowledge will greatly impact the efficiency of your business.
For instance, even if you build a great team of Machine Learning Engineers or Data Scientists, the models they build won't work optimally unless there are Software Engineers to back them who are capable of building the platforms they envision.
On the other hand, if you incorporate the right software engineers into your team from the start, they'll be able to communicate with other roles to better understand the prerequisites necessary to support the models they are building.
Macro vs. Micro
When considering a data engineer vs. software engineer, you have to think about the approaches they take.
A software engineer can build highly distributed and scalable systems and, because of their broader approach, software engineers are more common in smaller companies that don't have the capacity to hire for many roles. Therefore, software engineers are a common pick for leaner teams.
However, software engineers aren't as strong as data engineers when it comes down to the nitty-gritty aspects of data engineering. For instance, data warehouses and querying data are two common weak points of software engineers. Conversely, it is these two areas where data engineers really shine.
Compare Strong Points
If you are searching for a person who's primary focus is on pulling data from an API or other data source and then transform and move it around, you're seeking a data engineer. A good data engineer has skills when it comes to querying and modeling data along with working in data warehouses and making the most of visualization tools, including Looker and Tableau.
However, if you want someone who is a strong coder and has experience wiring with DevOps tools, a software engineer would be the better choice. Think about the tools and responsibilities this team member is taking on regularly to help you make your decision.
Who Should You Hire?
At the end of the day, it can prove to be a real challenge for your organization to determine which title is best between a data engineer vs. software engineer. It's not uncommon for even experienced hiring managers to post jobs looking for a data engineer when, in reality, the description is better suited to a software engineer or even a different role entirely.
As such, it's important to understand these roles so that you can pick the best person for the job. This isn't to say a data engineer isn't capable of working with Kubernetes or Docker; after all, engineers of all titles have found themselves in a time where they have to be proficient with a number of tools.
The most critical aspect you need to consider is the list of specific responsibilities you want your new team member to fill, which is the key deciding factor between a data engineer vs. software engineer. In many cases, teams would do best with both a data engineer and software engineer, along with a number of other roles.
After all, you have to remember that these roles are complementary, not interchangeable.
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Arm Your Team With The Right Tools
Ultimately, your business is trying to build a more efficient data science department, and that means not only hiring the right people but supplying them with the right tools. Whether you choose a data engineer vs. software engineer (or both), make sure your tech stack allows them to make the most of their skills.
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