Written by Coursera Staff • Updated on
Big data is changing the way we do business and creating a need for data engineers who can collect and manage large quantities of data.
Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It is a broad field with applications in just about every industry. Organizations have the ability to collect massive amounts of data, and they need the right people and technology to ensure it is in a highly usable state by the time it reaches data scientists and analysts.
In addition to making the lives of data scientists easier, working as a data engineer can give you the opportunity to make a tangible difference in a world where we’ll be producing 463 exabytes per day by 2025 [1]. That’s one and 18 zeros of bytes worth of data. Fields like machine learning and deep learning can’t succeed without data engineers to process and channel that data.
In this article, you'll learn more about data engineers, including what they do, how much they earn, and how to become one. But, if you'd prefer to start learning from working professionals right away, consider enrolling in IBM's Introduction to Data Engineering course.
What does a data engineer do?
Data engineers work in a variety of settings to 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 so that organizations can use it to evaluate and optimize their performance.
Some of the common tasks a data engineer might perform when working with data include:
Acquire datasets that align with business needs
Develop algorithms to transform data into useful, actionable information
Build, test, and maintain database pipeline architectures
Collaborate with management to understand company objectives
Create new data validation methods and data analysis tools
Ensure compliance with data governance and security policies
Working at smaller companies often means taking on a greater variety of data-related tasks in a generalist role. Some bigger companies have data engineers dedicated to building data pipelines and others focused on managing data warehouses—both populating warehouses with data and creating table schemas to keep track of where data is stored.
Want to hear from real-world data professionals? Listen to what practicing data engineers have to say about their jobs in this lecture from IBM's Data Engineering Professional Certificate:
What’s the difference between a data analyst and a data engineer?
Data scientists and data analysts analyze data sets to glean knowledge and insights. Data engineers build systems for collecting, validating, and preparing that high-quality data. Data engineers gather and prepare the data and data scientists use the data to promote better business decisions.
Read more: Data Analyst vs. Data Scientist: What’s the Difference?
Why pursue a career in data engineering?
A career in this field can be both rewarding and challenging. You’ll play an important role in an organization’s success, providing easier access to data that data scientists, analysts, and decision-makers need to do their jobs. You’ll rely on your programming and problem-solving skills to create scalable solutions.
As long as there is data to process, data engineers will be in demand. In fact, Dice Insights reported in 2019 that data engineering is a top trending job in the technology industry, beating out computer scientists, web designers, and database architects [2]. LinkedIn listed it as one of its jobs on the rise in 2021 [3].
Data engineer salary
Data engineering is also a well-paying career. The average base salary in the US is $119,985, with some data engineers earning as much as $185,000 per year, according to Glassdoor (March 2024) [4].
Data engineer career path
Data engineering isn’t always an entry-level role. Instead, many data engineers start off as software engineers or business intelligence analysts. As you advance in your career, you may move into managerial roles or become a data architect, solutions architect, or machine learning engineer.
Learn more about data engineering career opportunities in this lecture from IBM's Introduction to Data Engineering course:
How to become a data engineer
With the right set of skills and knowledge, you can launch or advance a rewarding career in data engineering. Many data engineers have a bachelor’s degree in computer science or a related field. By earning a degree, you can build a foundation of knowledge you’ll need in this quickly evolving field. Consider a master’s degree for the opportunity to advance your career and unlock potentially higher-paying positions.
Besides earning a degree, there are several other steps you can take to set yourself up for success.
1. Develop your data engineering skills.
Learn the fundamentals of cloud computing, coding skills, and database design as a starting point for a career in data science.
Coding: Proficiency in coding languages is essential to this role, so consider taking courses to learn and practice your skills. Common programming languages include SQL, NoSQL, Python, Java, R, and Scala.
Relational and non-relational databases: Databases rank among the most common solutions for data storage. You should be familiar with both relational and non-relational databases, and how they work.
ETL (extract, transform, and load) systems: ETL is the process by which you’ll move data from databases and other sources into a single repository, like a data warehouse. Common ETL tools include Xplenty, Stitch, Alooma, and Talend.
Data storage: Not all types of data should be stored the same way, especially when it comes to big data. As you design data solutions for a company, you’ll want to know when to use a data lake versus a data warehouse, for example.
Automation and scripting: Automation is a necessary part of working with big data simply because organizations are able to collect so much information. You should be able to write scripts to automate repetitive tasks.
Machine learning: While machine learning is more the concern of data scientists, it can be helpful to have a grasp of the basic concepts to better understand the needs of data scientists on your team.
Big data tools: Data engineers don’t just work with regular data. They’re often tasked with managing big data. Tools and technologies are evolving and vary by company, but some popular ones include Hadoop, MongoDB, and Kafka.
Cloud computing: You’ll need to understand cloud storage and cloud computing as companies increasingly trade physical servers for cloud services. Beginners may consider a course in Amazon Web Services (AWS) or Google Cloud.
Data security: While some companies might have dedicated data security teams, many data engineers are still tasked with securely managing and storing data to protect it from loss or theft.
Wondering where to start?
Build job-relevant data engineering skills with these popular courses on Coursera.
To learn Python, consider enrolling in the University of Michigan's Python for Everybody Specialization. If you're interested in learning more about databases and database management, try either Meta's Introduction to Databases or IBM's Introduction to Relational Databases (RDBMS) course.
If you need to build your understanding of machine learning, explore Open.AI and Stanford's Machine Learning Specialization.
2. Get certified.
A certification can validate your skills to potential employers, and preparing for a certification exam is an excellent way to develop your skills and knowledge. Options include the Associate Big Data Engineer, Cloudera Certified Professional Data Engineer, IBM Certified Data Engineer, or Google Cloud Certified Professional Data Engineer.
Check out some job listings for roles you may want to apply for. If you notice a particular certification is frequently listed as required or recommended, that might be a good place to start.
Read more: 5 Cloud Certifications for Your IT Career
3. Build a portfolio of data engineering projects.
A portfolio is often a key component in a job search, as it shows recruiters, hiring managers, and potential employers what you can do.
You can add data engineering projects you've completed independently or as part of coursework to a portfolio website (using a service like Wix or Squarespace). Alternately, post your work to the Projects section of your LinkedIn profile or to a site like GitHub—both free alternatives to a standalone portfolio site.
Brush up on your big data skills with a portfolio-ready Guided Project that you can complete in under two hours. Here are some options to get you started—no software downloads required:
Create Your First NoSQL Database with MongoDB and Compass
Database Design with SQL Server Management Studio (SSMS)
Database Creation and Modeling using MYSQL Workbench
Read more: How to Build a Data Analyst Portfolio: Tips for Success
4. Start with an entry-level position.
Many data engineers start off in entry-level roles, such as business intelligence analyst or database administrator. As you gain experience, you can pick up new skills and qualify for more advanced roles.See an example of a possible learning journey with this Data Engineering Career Learning Path from Coursera.
Do I need a degree to become a data engineer?
It’s not necessary to have a degree to become a data engineer, though some companies might prefer candidates with at least a bachelor’s degree. If you’re interested in a career in data engineering and plan to pursue a degree, consider majoring in computer science, software engineering, data science, or information systems.
Some bachelor’s degree programs offer a concentration in data engineering. The Bachelor of Science in Computer Science from the University of London, for example, features an optional module in databases and advanced data techniques.
Next steps
Start learning data engineering today with these top-rated courses from industry leaders and world-class universities:
For a beginner-level program, try the IBM Data Engineering Professional Certificate. Learn foundational data engineering skills and tools, like Python and SQL, while you complete hands-on labs and projects.
To prepare for an industry-recognized certification, explore Google Cloud's Data Engineering, Big Data, and Machine Learning on GCP Specialization. This intermediate-level program provides training in support of the Google Cloud Professional Data Engineer certification.
To advance your career, consider earning a bachelor's or master's degree in computer science. Although not always required, it is common for employers to seek job candidates with a computer science degree. Explore online an bachelor's degree from the University of London, or a master's degree from the University of Colorado Boulder.
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Frequently Asked Questions (FAQs)
While the aspects of a career that make it “good” will always be subjective, data engineering is an in-demand profession that offers a higher than average salary and relative job security. While Glassdoor identifies the average base salary for data engineers at $106,153, the U.S. Bureau of Labor Statistics (BLS) projects that the field will grow by eight percent between 2022 and 2032, adding a further 10,200 new jobs per year [4,5].
Yes, data engineers must code. Common coding languages that data engineers should know or be familiar with include Python, Java, R, SQL, NoSQL, and Scala.
Data engineers have the ability to work from home, though some employers might prefer or require employees to work on-site. Nonetheless, the nature of their work means that many data engineers can theoretically do their work from home.
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