What Does a Data Engineer Do (and How Do I Become One)?

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.
Female data engineer sits in front of a dual computer screen looking at data visualizations and writing in a notebook

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. Learn more about the role of a data engineer and find out how to become one.

What is data engineering?

Data engineering is the practice designing and building systems for collecting, storing, and analyzing data. 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 data infrastructure and personnel to sort and analyze the information.

What do data engineers 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

Working at smaller companies often means taking on a greater variety of data-related tasks. At a large organization, you’re more likely to specialize in building data pipelines and maintaining databases.

What’s the difference between a data analyst and a data engineer?

Data analysts analyze data sets to glean knowledge and insights. Data engineers build systems for collecting, validating, and preparing that high-quality data.

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 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 [1]. 

Data engineering is also a lucrative career. The average salary is $109,747, with some data engineers earning as much as $161,000 per year, according to Glassdoor [2].

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 Python, Java, SQL, NoSQL, R, and Scala.

  • 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. A course in machine learning can be a good starting point for exploring automation.

  • Database design and data processing techniques. You work with databases on a regular basis. Start with basics like SQL and database design.

  • 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.

2. Get certified. 

A certification can validate your skills to potential employers. Options include the Associate Big Data Engineer, Cloudera Certified Professional Data Engineer, IBM Certified Data Engineer, or Google Cloud Certified Professional Data Engineer.

Get started with Coursera

Prepare for the Google Cloud Certified Professional Data Engineer exam while earning a credential from an industry leader through the Data Engineering with Google Cloud Professional Certificate

3. Build a portfolio. 

Include any work completed in your degree program. Brush up on your big data skills with a portfolio-ready Guided Project that you can complete in under two hours.

4. Start with an entry-level position. 

Many data engineers start off in entry-level IT 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. 

Next steps

Build the essential skills you’ll need with courses from this Data Engineering Career Learning Path from Coursera. Whether you’re just getting started or looking to pivot to a new career, learn about how a degree in computer science can help you achieve your goals.

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Article sources

1. Dice. "Data Engineer Remains Top In-Demand Job, https://insights.dice.com/2019/06/04/data-engineer-remains-top-demand-job/." Accessed March 25, 2021.

2. Glassdoor. "Data Engineer Salaries, https://www.glassdoor.com/Salaries/data-engineer-salary-SRCH_KO0,13.htm." Accessed March 25, 2021.

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