Every day, the world creates 2.5 quintillion bytes of data. That’s 912.5 quintillion bytes each year — a staggering rate that’s only accelerating. With this massive rate of data generation in mind, it should be no surprise that more than 90 percent of the world’s data was generated in the last two years alone.
Data engineers are responsible for capturing, processing, and managing this growing ocean of data. In this post, we’ll break down the statistics, job requirements, and responsibilities of a career in data engineering.
Overview of Data Engineering
Companies of every size and industry need data to make business decisions. Businesses in every industry employ data scientists and data analysts to process and analyze raw data to gain actionable insights. But before these analytical professionals can work with this data, someone needs to capture it.
Data engineers are responsible for building systems that collect, manage, and convert raw data into usable information. The concept of data engineering is fairly new, with the data engineer role becoming more widespread around 2011. However, as a discipline, data engineering evolved from the more established field of information engineering, which dates back to 1976.
On a more technical level, the core job responsibilities of data engineers include:
- Writing code to obtain, manipulate, and process data
- Acquiring datasets for particular business goals
- Writing algorithms to process and transform data
- Building data validation and analysis tools
- Building and maintaining relational and non-relational databases
- Ensuring compliance with data privacy and security regulations
- Communicating with both technical and non-technical stakeholders
- Working with stakeholders to align systems to business objectives
- Keeping up-to-date with advancements in technology
What Kinds of Companies Hire Data Engineers?
Any company that’s looking to use data to make business decisions will need to hire data engineers to capture that data. With companies in every industry becoming increasingly data driven, the demand and opportunity for data engineers is endless. The top sectors hiring for data engineers include:
- Fortune 500: 21%
- Technology: 19%
- Finance: 9%
- Startups: 8%
- Professional Services: 6%
- Retail: 6%
- Media: 5%
- Internet: 5%
Types of Data Engineer Positions
The titles data engineers hold vary drastically, depending on their experience, education, and company. The title of a graduate from a coding bootcamp might look different than a candidate with a four-year degree. And the role of a data engineer in a five-person startup will be different than at a 5,000 person company.
At the beginning of their career, a data engineer will start out with an entry-level role, like junior data engineer or data engineering analyst. A new data engineer usually works in one of these roles for one to three years.
From there, they’ll have the opportunity to move into more senior-level and specialized roles with hands-on engineering experience. Data engineering job titles include:
- Data architect
- Machine learning engineer
- Business intelligence engineer
- Big data engineer
- Data warehouse engineer
- Technical architect
- Solutions architect
While they spend several years honing their skills, their responsibilities expand to include taking ownership of projects, working independently in a team environment, and mentoring project team members. Senior data engineers might also choose to specialize in a particular technology or discipline, such as big data, machine learning, and business intelligence.
With some experience under their belt, a data engineer often faces a crossroads in their career having to choose between two paths.
The first path is to pivot into people and team management functions. Hiring, mentoring, resource planning and allocation, strategy, and operations become a larger component of the responsibilities of data engineers pursuing this career path. At the higher levels of an organization, these job functions might include:
- Director of Data Engineering
- Data Engineering Manager
- Data Operations Manager
- Information Systems Manager
- Chief Information Officer (CIO)
- Chief Technology Officer (CTO)
The second possible career path is to continue as an individual contributor. Many data engineers opt to continue their careers as individual contributors, enjoying equally fulfilling careers and developing deeper technical expertise in various languages and frameworks.
The motivation behind this decision is that experienced data engineers may not be interested in or qualified to be managing a team. And engineers in an individual contributor role have the opportunity to focus on growing their technical skills and learning the newest emerging technologies. However, the career path for data engineers and the larger data science field as a whole is still being defined, as data engineering is still a relatively new field.
Salary Comparisons & Job Outlook
On average, data engineers receive highly competitive compensation packages. However, data sources on technical salaries often present vastly different, and at times conflicting, numbers at both a regional and global level. Estimates of average base salary for data engineers in the U.S. range from $116,427 to $131,871.
Junior data engineers can expect to occupy a lower salary band at the beginning of their career. In contrast, senior positions provide a higher average compensation, though data for this specific salary band is hard to find. Industry and company size also affect the salary band dramatically.
Current market conditions have made technical salaries especially volatile. Because of this, public salary data may be low or out of date. Total compensation packages, including equity and bonuses, are also changing rapidly. Hiring teams will need to conduct their own research to identify salary bands based on their company’s requirements and the technical needs of the role.
The job outlook for data engineers is equally promising. As the quantity of data the world produces accelerates, so too will the demand for engineers to process that data. In 2019, data engineering was the fastest-growing tech occupation, with a growth rate of 50%.
Requirements to Becoming a Data Engineer
Data engineers use a range of programming languages to work with data. These include, to name a few:
Many data engineering roles also require knowledge of cloud technologies such as AWS, Azure, or GCP. Data engineers might also need to have an understanding of machine learning, data APIs, and ETL (extract, transfer, load) tools.
Recruiters and hiring managers looking for data engineers should look for proficiency with in-demand tools and frameworks. These include:
- Kafka (data streaming)
- Cloudera Data (data management systems)
- Apache Airflow (workflow automation)
- Apache Hadoop (big data processing)
- Apache Spark (large-scale data processing)
- MySQL (relational database system)
- Oracle (relational database system)
- Cassandra (Non-relational database management system)
- Amazon Redshift (data warehousing)
- Azure (cloud analytics systems
- Google BigQuery (data warehousing)
It’s worth noting that there’s a degree of fluidity to the technologies that data engineers use. A framework that’s in demand today might be outdated a year from now.
Technical competency alone isn’t enough to succeed in a data engineering role. Mathematical, analytical, and problem-solving skills are a must in any technical role. And soft skills are even more critical in a digital-first or digital-only environment.
Employers may have a preference for data engineers with strong soft skills, such as:
- Time management
- Project management
- Problem solving
Communication skills, in particular, are critical to data engineering. Data engineers work with data scientists, data analytics, machine learning engineers, and developers on a regular basis. The ability to translate technical subject matter into digestible, actionable information that anyone can understand is highly valuable to data engineers — and the teams who employ them.
Experience & Education
After competency, the most important qualification for data engineers is experience. On-the-job experience and training is a critical requirement for many employers.
Then, there’s the question of education. 65% of data engineers have a bachelor’s degree and 22% have a master’s degree. Many employers still require data engineering candidates to have four-year degrees. But competition for skilled data engineers is fierce, and it’s common for job openings requiring degrees to go unfilled.
There are simply not enough engineers with degrees to fill thousands of open roles out there. Companies looking to hire data engineers will have access to a much larger pool of talent and achieve their data initiatives if they recognize other forms of education and experience.