Engineers are some of the most sought-after employees on the market. Some have even gone so far to proclaim that there’s a “war for engineering talent,” which has worsened with the pandemic. In June 2021, there were upwards of 365,000 job openings for IT workers, a large percentage of which included engineers.
But not all engineers are created equal. Though they are often discussed as a singular group, engineers have many different subspecialties, each of which fills a different role for the company. Many people often wonder the differences between a data engineer vs software engineer. One such example is the difference between data engineers and software engineers. While both roles may be in high demand, companies will choose to hire one or the other (or both) depending on their needs.
What are data engineers?
First, let’s start with data engineers.
Data engineers are responsible for the development, integrity, and maintenance of an organization’s data infrastructure. “Data infrastructure” will take on a different meaning at different organizations; a smaller company may rely on small-scale, low-maintenance databases, for example, while larger companies manage their data across multiple on-premise and cloud-based data platforms.
No matter the scale of data engineering, however, the goal is the same: to ensure a solid foundation of data for use in business-critical analytics or reporting. The work that data engineers perform in ensuring that data has been properly stored, cleansed, or integrated is fundamental to all organizational data initiatives.
Because data engineering best practices are constantly changing in the modern, big data era, data engineers must keep abreast of new trends. Over the past several years, the Internet of Things (IoT), serverless computing, hybrid cloud, AI, and machine learning (ML) have all made a huge impact on the data engineering landscape, just to name a few examples. Data engineers must carefully choose new technologies or strategies that are worth taking on, and implement a change management process to do so.
Though it may not be categorized as a “trend,” data accessibility continues to be placed at the forefront of data engineering strategy. Long gone are the days where data engineers were responsible for serving up all data requirements to business users. Instead, most data engineering teams have adopted user-friendly technologies that allow business users to access, integrate, and transform data themselves with clear data governance in place.
In the same vein, data engineers must collaborate with a wide range of roles, such as business analysts, data scientists, and data architects. Given a data engineer’s breadth of knowledge, their responsibilities will often overlap with these roles. Data engineers might work in the data science domain on machine learning projects, for example, or work closely with business users on an analytics project. In this way, data engineers serve as a critical data partner for just about all organizational functions.
What skills do data engineers need to have?
A proficient data engineer must be equipped with strong technical skills, which includes:
- Extensive knowledge of programming languages such as C#, Java, Python, R, Ruby, Scala and SQL. Python, R and SQL are widely considered the most important of the list.
- Experience with ETL/ELT tools and REST-oriented APIs, which are used for data integration.
- An understanding of a wide range of data systems and platforms, such as data warehouses and data lakes, relational databases, such as MySQL and PostgreSQL, and NoSQL databases and Apache Spark systems.
- An understanding of a wide range of business intelligence (BI) or business-facing platforms, which can be used to establish connections to data platforms and self-serve data to the business.
- A familiarity with machine learning (ML) skills. Though not typical to the day-to-day work of a data engineer, data engineers must be able to know the basics of deploying machine learning algorithms for insights.
What is a software engineer?
Now that we’ve discussed data engineers, let’s move on to software engineers.
The critical difference between a data engineer vs software engineer is obvious: while data engineers manage data infrastructure, software engineers build software. More specifically, software engineering is a branch of computer science that breaks down into two primary categories: applications software engineers and systems software engineers.
- Applications Software Engineers
Responsible for creating internal or external applications or platforms that a user can interact with. In collaboration with developers, product managers, product designers, and marketers, application software engineers will make tweaks to the product and continuously release updated versions. - Systems Software Engineers
A systems software engineer is responsible for creating and maintaining an organization’s computer systems. This can include setting up networks, building operating systems for user-facing applications, or maintaining IT documentation.
Another important distinction in the realm of software engineering is the difference between “front-end” and “back-end” development. While front-end refers to all of the software development work involved in creating an intuitive user interface, back-end refers to the maintenance of servers and systems that deliver information to the user. Applications software engineers may be involved in both front-end and back-end engineering, while systems software engineers are strictly involved in back-end work.
As a practice, software engineering incorporates a number of disciplines, which include, requirements gathering, software design, software configuration management, software engineering models and methods, software quality, as well as core computing and mathematical concepts. It is a highly technical field that requires sharp focus and a deep understanding of programming languages.
However, despite its technical nature, a good software engineer will also have strong interpersonal skills. A software engineer needs to gather requirements from a number of different stakeholders across the organization, collaborating with each one to determine the final, prioritized list for a software engineer and her team. The ability to advocate for a certain strategy while listening to and incorporating everyone’s desires is key.
What skills do software engineers need to have?
In order to succeed as a software engineer, an engineer must have the following skills:
- Extensive coding programming knowledge, including Python, Java, C/C++ and Ruby, along with the fundamentals of data structures and algorithms.
- Experience in (and the patience for) software testing and debugging, which is one of the most critical responsibilities of a data engineer. The ability to write code quickly is meaningless if that code is not properly debugged and/or tested. Though AI programs have extracted some of the manual nature of this process, it remains important to the role.
- The ability to apply object-oriented design (OOD) guidelines in order to create software that’s modular and reusable. The approach has gained popularity in recent years, and involves defining objects and their subsequent interactions in order to solve a software problem.
- Problem-solving and attention to detail are important soft skills for a software engineer. Day in and day out, software engineers will be immersed in repetitive, but complex code—they need to be able to pay close attention to prevent small errors.
- Communication and management skills are equally important for a software engineer. As mentioned, software engineers need to be able to routinely collaborate with a wide variety of stakeholders. In addition, they will also eventually manage a team of developers and need to be able to direct them effectively in order to stay on task.
Data engineer vs software engineer salary
According to Indeed, the average salary for a data engineer is $118,386, while the average salary for a software engineer is only slightly lower at $115,190. However, these numbers will vary greatly depending on a wide variety of factors, such as location, company, or experience. For example, after honing in on San Francisco, a tech hub with a high cost of living, both of these numbers increase significantly—data engineers are paid $135,748 on average, while software engineers are paid $129,651 on average.
The meeting point of data engineers vs software engineers
Though data engineers and software engineers have distinct responsibilities that don’t often overlap in their day-to-day work, their goals often converge. For example, consider the increasing amount of technology applications that heavily rely on data—Facebook, Uber, or Kayak are all examples that come to mind.
In these cases, software developers must create a usable, interactive interface, but the engine of these applications is run on data. Facebook would be nothing without its complex algorithms that present the right ads to the right users; Uber needs a huge amount of data in order to calculate which drivers are available at the right time; Kayak is constantly monitoring flight data to present the best offers to its users. Data engineers (and data scientists) are a huge part of making these applications successful, as much as software developers are for the front-end development of their interfaces.
As data becomes more and more integral to just about all software or application offerings, we’ll see an increased joint effort between data engineers and software engineers. Though data engineer vs software engineers still have individual goals, the end product will be a result of both types of engineering.
Getting started
If you’re interested in a career in data engineering or software engineering, you’re in a good place to be—there is high demand for both roles, and that trend shows no signs of slowing.
While many software engineers have four-year university degrees specialized in computer programming, “coding bootcamps” have gained traction in the past several years as a means to gain the skills required for software engineering in a short amount of time. Typically, they are intensive eight- to 30-week programs that are designed to help students enter the job market as soon as possible.
Since data engineers also need to be well-versed in programming languages, four-year university degrees or coding bootcamps are an option for them as well. Data engineers will often start out as an IT assistant to get firsthand experience about back-end systems. From there, data engineers can build on their experience with professional certificates, such as the Certified Data Management Professional (CDMP) credential, offered by the Data Management Association (DAMA) International.
Interested in learning more about data engineering? Our data engineering platform is a great way for both new and seasoned data engineers to improve their work. Check out our blog to learn more about the platform and related topics.