Data Engineer vs. Software Engineer: Choosing the Right Career Path

Written by Coursera • Updated on

What’s the difference between a data engineer and a software engineer? Here’s what you need to know to decide which role is right for you.

[Featured image]. Data Engineers study charts and graphs on whiteboard

Data engineer and software engineer—these two data science job titles might sound similar, but each role has its own distinct responsibilities and collaborates with different stakeholders. Data engineers focus on creating frameworks and systems for analyzing data, while software engineers build products such as apps or websites.

In this article, we’ll unpack the difference between data engineers and software engineers to help guide you through your career search.

A quick guide to engineering roles

When you’re browsing for job openings, especially in data science and technology, you’ll likely see different roles that include the world “engineer.” It can be difficult to decipher the exact differences between the two roles from just reading job descriptions. Let’s take a quick look at four common engineer roles within the tech industry.

  • Data engineer: Data engineers build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible for organizations to optimize their performance.

  • Software engineer: Software engineers, sometimes called software developers, create software for computers and applications.

  • Machine learning or AI engineer: Machine learning engineers research, build, and design the AI models and algorithms responsible for improving existing AI systems. They focus only on the aspect of AI that trains machines to think like humans, since machine learning falls under AI.

  • Systems engineer: A systems engineer develops and oversees repairs for systems, solving problems and innovating for improvement.

You’ll likely have heard of “engineer” roles in sectors not related to data science. Mechanical engineers build devices, machines, and tools; electrical engineers design and test the manufacturing of electrical equipment; and civil engineers design and build infrastructure.

Do you sense a theme here? Whether it’s data or robots, engineering involves applying science and mathematics to solve real world problems. That includes designing and developing innovative products and processes across industries and applications.

Data engineer vs. software engineer: what’s the difference?

The biggest difference between data engineering and software engineering is the scope of work. Data engineers build data systems and databases while software engineers create applications, software, and other products. A data engineer typically works with big data to create the infrastructure so data analysts, data scientists, and business analysts can maneuver the data for their specific needs.

Here’s a breakdown of the main differences.

Data engineerSoftware engineer
Build data systems and databases that can store, consolidate, and retrieve dataBuild systems, applications, websites, and tools
Specialized roleBroader role
Users are data scientists or analystsUsers are general public
Skills include coding and development, optimizing queries, distributed computing, building data pipelines, machine learningSkills include building operating systems, coding, programming languages, storing information on databases, data modeling
Works with data scientists, business analysts, project managers on a data science teamWorks with designers, programmers, and developers
Popular tools include Tableau, Looker, Amazon Redshift, Apache Spark, Kafka, Hadoop, Hive, and morePopular tools include Git, GitHub, Stack Overflow, Jira, Amazon Web Services, and more

Read more: What Is a Data Engineer?: A Guide to This In-Demand Career

Key differences

Data engineers build systems for storing and retrieving the data that is required for the systems and applications that software engineers build. This field emerged as a specialized skill set from software engineering, as data engineers are responsible for making accurate data available to data scientists and analysts. 

Software engineers develop operating systems, mobile apps, and software design using front- and back-end development. These engineers operate at a broader level, building the infrastructure or platform that imports and stores the data for a website, app, or software.

Though the two career paths have similar skills, their approaches and goals are very different. 

Tasks and responsibilities

With such different end-goals, data and software engineers spend their time collaborating with different teams within the company.

Day-to-day tasks for a data engineer might include:

  • Acquiring datasets that align with business needs

  • Developing algorithms to transform data into actionable insights

  • Building, testing, and maintaining database pipeline architectures

  • Collaborating with management to fulfill company objectives

  • Creating new data validation methods and data analysis tools

Day-to-day tasks for a software engineer might include:

  • Designing and maintaining software systems

  • Evaluating and testing new software programs

  • Optimizing software for speed and scalability

  • Writing and testing code

  • Consulting with clients, engineers, security specialists, and other stakeholders

Data engineer vs. software engineer salary

Your earning potential as a data engineer or software engineer depends on a variety of factors, including your location, education, experience, and industry. Generally speaking, both career paths are high earning and competitive. Here’s a look at how three different sources report average or median salaries in the US.

US Bureau of Labor StatisticsGlassdoorPayscale
Data engineer$101,000 median salary (2021) 1$110,402 average total salary (2022) 3$93,637 average base salary (2022) 5
Software engineer$109,020 median salary (2021) 2$105,583 average total salary (2022) 4$89,086 average base salary (2022) 6

Education requirements

To become a data or software engineer, your educational background will be rather similar. A bachelor’s degree in computer science, information technology, or another related field would help you land an entry-level position in either career field. 

Here’s a rough breakdown of degrees commonly held by data and software engineers:

Degree or diplomaData engineer 7Software engineer 8
Bachelor’s65%73%
Master’s22%20%
Associate7%4%
Doctorate2%0%

Certifications can also help you break into data or software engineering. For those taking a less traditional educational path, you might be interested in the combination of a high school diploma or associate’s degree plus a certification. Earning this type of credential is proof that you’ve mastered a certain skill set.

Data engineer certifications:

  • Associate Big Data Engineer

  • Cloudera Certified Professional Data Engineer

  • IBM Certified Data Engineer

  • Google Cloud Certified Professional Data Engineer

Software engineer certifications: 

  • Certified Software Development Professional (CSDP)

  • Certified Software Engineer

  • C Certified Professional Programmer (CLP)

  • C++ Certified Professional Programmer (CPP)

  • AWS Certified Developer

  • Microsoft Certified: Azure Fundamentals

Skills needed to be a data and software engineer

The skills required for data and software engineers overlap. So if you’re unsure of which career path you’d like to take, there are plenty of skills you can learn right now to become job ready.

Data engineer skills:

  • Coding (programming languages such as SQL, Python, Java, R, and Scala)

  • Relational and non-relational databases

  • ETL (extract, transform, and load) systems

  • Data storage

  • Automation and scripting

  • Machine learning

  • Big data tools, such as Hadoop, MongoDB, and Kafka

  • Cloud computing

Software engineer skills:

  • Coding languages like Python, Java, C, C++, or Scala

  • Database architecture

  • Agile and Scrum project management

  • Operating systems

  • Cloud computing

  • Version control

  • Design testing and debugging

Want to learn more?Learning Data Engineer Skills: Career Paths and Courses

Which type of engineering is right for me?

If you get excited about building things in the technology sector, then becoming a data engineer or a software engineer could be a good fit. Which type of engineer will depend on your unique skills and interests.

If you’re passionate about building and managing data systems to fulfill business needs or goals, then you might be better suited for a data engineer role. If you enjoy collaborating with teams to produce systems, apps, or websites, then becoming a software engineer could be more attractive.

If software engineering is the right path for you, learn more: The Job Seeker’s Guide to Entry-Level Software Engineer Jobs

Become an engineer with Coursera

Now that you’ve learned the difference between a data engineer and a software engineer, are you ready to kickstart your career? Consider enrolling in IBM’s Data Engineer professional certificate or DevOps and Software Engineering professional certificate to gain the skills and knowledge you need to elevate your data science career. 

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IBM Data Engineering

Launch your new career in Data Engineering. Master SQL, RDBMS, ETL, Data Warehousing, NoSQL, Big Data and Spark with hands-on job-ready skills.

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Average time: 5 month(s)

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Skills you'll build:

Relational Database Management Syste (RDBMS), ETL & Data Pipelines, NoSQL and Big Data, Apache Spark, SQL, Data Science, Database (DBMS), NoSQL, Python Programming, Data Analysis, Pandas, Numpy, Information Engineering, Jupyter notebooks, Web Scraping, Extract Transform Load (ETL), Database (DB) Design, Database Architecture, Postgresql, MySQL, Relational Database Management System (RDBMS), Cloud Databases, Shell Script, Bash (Unix Shell), Linux, Database Servers, Relational Database, Database Security, database administration, Extraction, Transformation And Loading (ETL), Apache Kafka, Apache Airflow, Data Pipelines, Data Warehousing, Cube and Rollup, Business Intelligence (BI), Star and Snowflake Schema, cognos analytics, Mongodb, Cloud Database, Cloudant, Cassandra, Apache Hadoop, SparkSQL, SparkML, Big Data, Relational Databases

Article sources

1. US Bureau of Labor Statistics. “Database Administrators and Architects, https://www.bls.gov/ooh/computer-and-information-technology/database-administrators.htm.” Accessed September 16, 2022.

2. US Bureau of Labor Statistics. “Software Developers, Quality Assurance Analysts, and Testers, https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm.” Accessed September 16, 2022.

3. Glassdoor. “How much does a Data Engineer make?, https://www.glassdoor.com/Salaries/data-engineer-salary-SRCH_KO0,13.htm.” Accessed September 16, 2022.

4. Glassdoor. “How much does a Software Engineer make?, https://www.glassdoor.com/Salaries/software-engineer-salary-SRCH_KO0,17.htm.” Accessed September 16, 2022.

5. Payscale. “Average Data Engineer Salary, https://www.payscale.com/research/US/Job=Data_Engineer/Salary.” Accessed September 16, 2022.

6. Payscale. “Average Software Engineer Salary, https://www.payscale.com/research/US/Job=Software_Engineer/Salary.” Accessed September 16, 2022.

7. Zippia. “Data Engineer Education Requirements, https://www.zippia.com/data-engineer-jobs/education/.” Accessed September 16, 2022.

8. Zippia. “Software Engineer Education Requirements, https://www.zippia.com/software-engineer-jobs/education/.” Accessed September 16, 2022.

Written by Coursera • Updated on

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