Data — the new gold rush. The rise of the big data era has opened many opportunities for those with the skills and knowledge to work in this field. Moreover, companies are increasingly investing in data engineering teams to ensure they can get the most out of their information. With data becoming increasingly crucial for business decisions and process improvements, the need for data engineers has grown exponentially.
If your company wants to leverage data and turn it into actionable insights, you will need to hire a data engineer.
What Is Data Engineering?
Before we can tell you what a data engineer does, you'll need to understand the field of data engineering itself. Data engineering is a broad field that focuses on data collection, storage, access, and manipulation. Data engineering involves designing and building complex systems that manage data and data-driven decisions and processes.
Today, companies use data engineering to improve efficiency and performance, increase customer satisfaction, and optimize profitability. Therefore, data engineers must understand the business needs of the company they are working for and have extensive data science and engineering knowledge. Some real-life applications of data engineering include:
- Banking sector: Detecting fraud and managing customer accounts
- Retail sector: Predicting customer preferences, trends, and sales patterns
- Marketing sector: Segmenting customers and creating personalized offers
- Education: Grading systems analytics to monitor student performance
- Healthcare sector: Adjusting treatments and preventive plans
- Media and entertainment: Optimizing content delivery and personalization
What Is a Data Engineer?
A data engineer is a technical expert who develops, maintains, and optimizes data systems to ensure they are secure and reliable. They work with data from different sources, ranging from traditional databases to the cloud, to create a data pipeline that is both secure and efficient. They also develop algorithms and scripts to automate data processes.
What Does a Data Engineer Do?
Generally, the role of a data engineer is to ensure that data systems are secure and reliable and optimize them for better performance. However, a data engineer may have many other roles and responsibilities depending on the organization. These roles fall into three broad categories:
- Generalist: Works for a small team or company and is generally responsible for the entire data system. A generalist data engineer may have to do everything from data modeling and database administration to data analysis and report generation.
- Pipeline-centric: Focuses on creating and maintaining data pipelines that connect different data sources. This data engineer works in a mid-sized team and is responsible for creating a secure data flow between various sources.
- Database-centric: Focuses on database administration, optimization, and maintenance. They work in larger teams and are responsible for data security, scalability, performance optimization, and troubleshooting.
A data engineer is responsible for developing and maintaining data systems, including designing, creating, testing, and managing data solutions such as databases, data warehouses, data lakes, and more. Once hired, they will typically be responsible for the following tasks:
- Design, develop and maintain data storage solutions
- Clean and organize data from different sources
- Develop scripts to automate data processes
- Monitor data quality and integrity
- Analyze large data sets to identify trends
- Develop algorithms to improve data processing speed and accuracy
- Optimize data systems for better performance
- Provide technical support and troubleshooting
Data Engineer vs. Data Scientist
When you read job postings and descriptions for data engineer and data scientist roles, you may think they are the same role. While the two have many similarities, there are key differences. They work together and complement each other, but each serves a different purpose.
Generally, data engineers focus on the technical side of data management and analysis, while data scientists focus on the application. Data engineers are responsible for creating and managing data systems, and data scientists analyze data to gain insights and develop predictive models.
Data scientists use their knowledge of data to create models that can be used for predictive analytics and decision-making. In contrast, data engineers use their technical skills to build data systems that other team members can use.
Data Engineer Skills & Educational Background
Before hiring a data engineer, you need to know what skills they should possess and what education they must have.
Data Engineer Skills
There are several technical and soft skills essential for a data engineer. Technical skills include the following:
- Programming skills, such as Java, C++, and Python
- NoSQL databases, such as MongoDB and Cassandra
- MS Windows, UNIX, Solaris, and Linux operating systems
- Database management and optimization
- Data modeling, data warehousing, and ETL
- Cloud computing systems, such as AWS or Azure
- Experience with data mining and analysis
- Machine Learning (ML) programs such as SAS, R, and MatLab
- Proficiency with cloud computing with Microsoft PowerBI and Azure to gain valuable insights into business analytics and intelligence
- Familiarity with algorithms for predictive modeling, natural language processing, and text analysis
Soft skills include the following:
- Ability to work independently and in a team environment
- Strong communication and problem-solving skills
- Proactive attitude
- Ability to adapt to new technologies
- Creativity
- Attention to detail
Education
Entry-level data engineers typically have a bachelor's degree in Computer Science, Information Technology, or related fields. However, some employers may accept candidates with a degree in Mathematics or Statistics. These education qualifications may change depending on the level of the role you want to fill. For instance, a mid-level or senior-level data engineer position may require a higher degree, such as a master's or doctorate.
There are also some certifications available for data engineers. These certifications focus on data management, architecture, and security and can help demonstrate a candidate's proficiency. These certifications include:
- Microsoft Certified: Azure Data Engineer Associate
- AWS Certified Data Analytics - Specialty
- Google Professional Data Engineer
- Certified Big Data Architect/Engineer from IBM
- Cloudera Data Platform Generalist Certification
Data Engineer Salary
The salary of a data engineer can vary greatly depending on the company, experience, and location. While there is no definite figure for how much a data engineer in the U.S. makes, several sites have provided some estimates:
- According to Glassdoor, the average annual salary for a data engineer in the U.S. was $117,721 in January 2023.
- On ZipRecruiter, big data engineer salary estimates a national average of $125,662.
- Indeed.com listed the average annual salary of a data engineer in the U.S. as $127,360 in January 2023.
Based on the figures above, the average salary of a data engineer in the U.S. is between $60,000 and $140,000. However, this figure can vary depending on a data engineer's experience, and leadership level.
Data Engineering Salaries From Top Companies
Data engineers are in high demand, so companies often offer generous salaries to attract and retain the best talent. Here are some salaries from popular companies:
- Amazon data engineer: $120,000 - $210,000
- Microsoft data engineer: $120,000 - $183,000
- Google data engineer: $120,000 - $201,000
- Facebook data engineer: $140,000 - $230,000
Advantages of Hiring a Data Engineer
Hiring a data engineer can bring your organization many tangible and intangible benefits, including:
- Improved data security: A data engineer can develop secure and reliable data systems to ensure your data is safe while making it easier to access and manage.
- Automation and optimization: Data engineers can develop scripts to automate data processes, which saves time and resources. They can also optimize data systems for better performance.
- Improved data quality: Data engineers can ensure your organization's data is accurate and up-to-date.
- More efficient data analysis: Data engineers can develop algorithms and scripts to analyze data quickly and gain insights. This will help you make decisions faster and improve the efficiency of your organization.
- Cost savings: Hiring a data engineer can help you reduce costs, as they can develop data solutions tailored to your specific needs.
Hire Data Engineers With Revelo
The role of a data engineer is becoming increasingly important as data becomes more critical for decision-making. Companies should consider investing in the right people to ensure they can get the most out of their data.
If you want to leverage data and get the most out of your information, you need a data engineer. At Revelo, we specialize in helping you find the best data engineering talent for your team. We match companies with talent who become full-time hires and eventually join clients' teams, so you get the most out of their expertise. Contact us today to learn how we can help you hire top data engineers.
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