With businesses increasingly relying on data for their day-to-day operations, the role of a data engineer has emerged as one of the most sought-after professions in the industry. But what does a data engineer do exactly? And why is it in demand? According to McKinsey, by 2025, smart workflows and seamless interactions between humans and machines will likely become a new standard. Most employees will use data to optimize nearly every aspect of their work. This has led to a surge in demand for data professionals who can effectively handle and manage this growing data. However, despite the growing need for data-driven roles such as data engineering and data scientists for almost a decade, there is still a significant gap between the demand and supply of skilled data professionals in the job market. Therefore, if you are looking to pursue data engineering, now is a great time to explore this exciting career path. This article provides a complete overview of data engineering—including the skills, qualifications, salary, and career outlook. So let’s dive in and understand what does a data engineer do, and how to become one!
What Does a Data Engineer do?
Data engineers play a crucial role in designing, constructing, and maintaining the systems used to control, manage, and organize raw data that can then be transformed into high-quality data for analysis. They work closely with data analysts and data scientists and share the prepared data set for further analysis to help organizations make smart decisions and optimize their performance. Furthermore, data engineering roles typically fall into three categories: pipeline-centric, database-centric, and generalist. Pipeline data engineers are responsible for building data pipelines used to collect the data. Database data engineers manage the operation of a data warehouse across multiple databases for data sorting. Generalists are responsible for every step of the data process and typically work for small businesses.
Data Engineer Roles and Responsibilities
- Collecting, organizing, managing, and converting raw data into a format that can be easily analyzed by data analysts and scientists
- Building and maintaining data pipelines that collect and transport data from various sources to the organization’s data storage systems
- Using algorithms and programming languages such as SQL and Python to prepare data for analysis
- Working closely with the management to understand and address business requirements related to data storage, management, and analysis
- Creating data analysis tools and developing new data validation methods to ensure data accuracy and completeness
- Identifying ways to make data more reliable, efficient, and accessible to relevant stakeholders
- Creating and maintaining the organization’s software and hardware architecture to support efficient and secure data storage and management
- Conducting research and troubleshooting to address potential problems that may arise in the data storage and management systems
Now that you know what does a data engineer do, let’s move on to how you can pursue a career in this field.
Data Engineer Skills and Qualifications
Data engineering demands a strong educational background to build a successful and sustainable career. These professionals typically have a Bachelor’s Degree in Software Engineering, Computer Science, IT, or a related field. In addition to formal education, data engineers must be proficient in programming languages and have experience working with data. Furthermore, data engineers should possess a diverse set of technical skills to succeed in the field. Some of the most important skills for data engineers include:
- Expertise in SQL
- Knowledge of data warehousing and Extract, Transform, Load (ETL) tools
- Ability to design and develop data storage solutions
- Familiarity with big data tools such as MongoDB, Kafka, and Hadoop
- Understanding of cloud computing tools such as AWS, Azure, and GCP
- Experience in automation and scripting
- Knowledge of machine learning
- Familiarity with data transformation tools such as InfoSphere, Hevo Data, Talend, and Pentaho Data Integration
- Expertise in data visualization to communicate insights effectively
Demand for a Data Engineer
As organizations continue to generate large amounts of data, the demand for skilled data engineers who can manage and transform this data into meaningful insights is expected to increase. The U.S. Bureau of Labor Statistics classifies data engineers as part of computer and information scientists, with a projected job growth of 21%, and mathematicians and statisticians, with a projected job growth of 31%, indicating a high demand for data engineering careers.Moreover, according to Glassdoor’s list of 50 Best Jobs in America for 2022, data engineers ranked seventh among the top jobs in the U.S. The ranking is based on job openings, job satisfaction, and salary. With over 11,821 job openings and a job satisfaction rating of 4 out of 5, data engineering makes for a highly lucrative career opportunity.
Data Engineer Salary
Data engineering is a highly technical and in-demand field offering highly lucrative earning opportunities. According to Glassdoor, here’s how much a data engineer earns on an average the world over:
|Average Annual Salary
Now that we have covered what does a data engineer do, their skills, qualifications, career, and salary overview, let’s look at some frequently asked questions about data engineering.
Frequently Asked Questions
How Does a Data Engineer Differ From a Data Scientist?
The key difference between a data engineer and a data scientist lies in their roles and responsibilities in data analysis. Data scientists typically analyze and interpret data to extract insights and solve business problems. On the other hand, data engineers are responsible for building and maintaining the underlying infrastructure that supports the data science process, such as data pipelines and storage architectures. In short, data engineers lay the foundation for data analysis, while data scientists use that foundation to extract insights and make informed business decisions.
What are Some Common Tools and Technologies Used by Data Engineers?
Some of the most common data engineering tools and technologies data engineers use include Python, Apache Spark, Apache Airflow, Apache Kafka, SQL, PostgreSQL, MongoDB, Amazon Redshift, Tableau, and Power BI.
How Do Data Engineers Manage Scalability Issues with Data Processing?
Data engineers manage scalability issues with data processing through various methods, such as horizontal and vertical scaling. To do so, they must continually monitor and optimize the system to handle large volumes of data and maintain high performance.
What are the Challenges Faced by Data Engineers in Today’s Data-Driven World?
Data engineers face a wide range of complex challenges. These include maintaining and supporting data pipelines, scalability, security, quality, and governance. Moreover, data engineers must keep up with continuous learning to stay up-to-date with the latest technologies and tools, as the data engineering landscape constantly evolves.
ALSO READ: The Ultimate 2023 Data Science Roadmap: A 6-Step Guide to Success To conclude, this guide on “what does a data engineer do” provides a detailed overview of data engineering. With the explosion of data in today’s world, data engineers are in high demand, and this trend will continue in the coming years too. More than ever, companies need competent data engineers to build the infrastructure and understand data. If you are thinking about advancing your career in data engineering and gaining a competitive edge in the marketplace, this is a great time to start. Explore these data science courses offered by Emeritus to advance your skills and further your career.
By Krati Joshi
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