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

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.

[Featured Image] Female data engineer sits in front of a dual computer screen, looking at data visualizations and writing in a notebook.

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.

These are some common tasks you might perform when working with data:

  • 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.

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.


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. According to the Information and Communication Technology Council (ICTC), employment within the digital economy in Canada will continue growing at a rate of 2.22 per cent through 2025, a rate higher than the general economy’s 1.97 per cent [2] Additionally, ICTC predicts that digital jobs, including those working directly with data, will account for 11 per cent of employment in the country, with demand of approximately 250,000 new jobs [2]. 

Data engineer salary

Data engineering is also a well-paying career. The average salary in Canada is $98,271, with some data engineers earning as much as $122,000 per year, according to Glassdoor [3]. 

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.

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 college diploma or bachelor’s degree in a related field, such as a Bachelor of Science in Computer Science. 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.

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). Alternatively, 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:

4. Start with an entry-level position. 

Many data engineers start off in entry-level roles, such as a junior analyst or an assistant to any similar IT position. 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.

Next steps

Whether you’re just getting started or looking to pivot to a new career, start building job-ready skills for roles in data with the Google Data Analytics, IBM Data Science, or IBM Data Engineering Professional Certificates. 

Article sources


World Economic Forum. "How much data is generated each day?, https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-day-cf4bddf29f/." Accessed May 3, 2024.

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