They’re often tasked with managing big data. 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.īig data tools: Data engineers don’t just work with regular data. You should be able to write scripts to automate repetitive tasks. 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.Īutomation and scripting: Automation is a necessary part of working with big data simply because organizations are able to collect so much information. Common ETL tools include Xplenty, Stitch, Alooma, and Talend.ĭata storage: Not all types of data should be stored the same way, especially when it comes to big data.
You should be familiar with both relational and non-relational databases, and how they work.ĮTL (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. Relational and non-relational databases: Databases rank among the most common solutions for data storage.
Common programming languages include SQL, NoSQL, Python, Java, R, and Scala. Learn the fundamentals of cloud computing, coding skills, and database design as a starting point for a career in data science.Ĭoding: Proficiency in coding languages is essential to this role, so consider taking courses to learn and practice your skills. Consider a master’s degree for the opportunity to advance your career and unlock potentially higher-paying positions.īesides earning a degree, there are several other steps you can take to set yourself up for success. By earning a degree, you can build a foundation of knowledge you’ll need in this quickly-evolving field. Many data engineers have a bachelor’s degree in computer science or a related field. With the right set of skills and knowledge, you can launch or advance a rewarding career in data engineering. As you advance in your career, you may move into managerial roles or become a data architect, solutions architect, or machine learning engineer.
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Instead, many data engineers start off as software engineers or business intelligence analysts. Data engineer career pathĭata engineering isn’t always an entry-level role. The average salary in the US is $115,176, with some data engineers earning as much as $168,000 per year, according to Glassdoor (May 2022). Data engineer salaryĭata engineering is also a well-paying career. LinkedIn listed it as one of its jobs on the rise in 2021. 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. You’ll rely on your programming and problem-solving skills to create scalable solutions.Īs long as there is data to process, data engineers will be in demand. 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. Data Scientist: What’s the Difference? Why pursue a career in data engineering?Ī career in this field can be both rewarding and challenging. Listen to some practicing data engineers talk about what they do. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance. What does a data engineer do?ĭata 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. Fields like machine learning and deep learning can’t succeed without data engineers to process and channel that data. That’s one and 18 zeros of bytes worth of data. 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.
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. It is a broad field with applications in just about every industry. Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale.
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