What Is an Analytics Engineer?

By some estimates, our society produces at least 2.5 quintillion bytes of data each and every day. This data generation has been driven by many factors, including new social media platforms, mobile apps, a shift towards digital payment, and the rapid adoption of various internet-connected devices (known as the Internet of Things). 

While much of this data may seem random and—to the untrained—useless, it holds a tremendous amount of potential for modern businesses and organizations. The most successful businesses of the past two decades have been those that have been able to collect, clean, organize, and analyze this data, and use it to make strategic decisions.

That fact has in and of itself has led to an explosion of demand for professionals with demonstrated “data” skills.

Though the field has thus far been dominated by professionals working as data scientists, data analysts, and data engineers, a new profession has in recent years begun to take shape: an analytics engineer. 

Below, we explore this exciting new career and answer the most common questions that individuals considering this path might have about the field. Our goal is to help you fully understand the various parts of a modern data team so that you can find the role that best fits your personal career goals and objectives. 

What is an analytics engineer?

An analytics engineer, also known as a data analytics engineer, is a member of the modern data team. They can be found within organizations of any size, though they are particularly common in larger organizations. 

The job title of “analytics engineer” is admittedly similar to that of data engineer, data scientist, and data analyst. It’s important to recognize that it is, however, its own unique discipline—a new and evolving role that acts as an intersection between these other individuals. As such, the analytics engineer works to support each of these roles, empowering them to make better use of the data at the organization’s disposal in order to do their jobs more effectively.

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What does an analytics engineer do?

To understand what an analytics engineer does and how the job differs from the others mentioned above, it is first important to understand how different members of a data team interact with data. 

Data engineers are software engineers responsible for actually building and maintaining the datasets that will eventually be used by data analysts and data scientists. As such, data engineers essentially prepare the infrastructure that the data team relies upon. This involves:

• Collecting, aggregating, and integrating data from a variety of sources
• Building the data platform that will be used by other members of the data team
• Optimizing the data warehouse as necessary

Data analysts perform a range of tasks that are primarily associated with various forms of reporting. For example, a data analyst might be responsible for:

• Analyzing data in order to extract insights which might help guide the business’s strategic planning
• Using existing data to forecast future performance
• Building and maintaining performance dashboards
• Preparing reports, visualizations, and presentations as necessary to communicate their insights with the broader organization
• Documenting their various processes so that other members of the organization understand how insights were gathered

Data scientists, on the other hand, are responsible for larger-scale analysis. They use advanced modeling techniques—such as machine learning, statistical modeling, and artificial intelligence—to analyze and glean insights from large datasets for their organization. Some of the discrete tasks that data scientists are often responsible for performing include:

• Identifying which of an organization’s data analytics problems might offer the greatest ROI if pursued 
• Identifying which datasets and variables are required for a given data project
• Aiding the data engineer in the collection of raw data
• Designing models and algorithms which will be used to mine and organize data
• Leveraging artificial intelligence and machine learning where possible to streamline their processes

In this team structure, the analytics engineer falls in between the data engineer and the data analyst. They are responsible for performing a range of duties that supports both of those roles, including:

• Taking the data compiled by the data engineer and cleaning it so that it complies with data hygiene best practices
• Organizing and transforming the data in a meaningful way and providing additional context as necessary so that it is ready for analysis
• Working with the data engineer to streamline the processes outlined above so that data is cleaner earlier in the process
• Maintaining documentation related to datasets and analysis and ensuring that everyone on the data team uses the same language and definitions
• Aiding in the development and design of analytics projects as necessary
• Discovering opportunities for an organization to improve its systems, enterprises, and processes through the use of data analytics 
• Working with the organization’s technology team to build and maintain complex databases
• Creating integrated views of data collected from multiple sources
• Developing and using tools, algorithms, and processes for data mining and data visualization to generate reports to be used in decision making
• Training other members of the organization (business analysts, key stakeholders) as necessary so that they are capable of using various data tools and communicating with other members of the data team

The skills required to become an analytics engineer clearly overlap with those required of data engineers and analysts. Some of the most important skills include software engineering, data warehousing, data extraction, and modeling. Analytics engineers also tend to be skilled in using tools such as Reshift, Snowflake, BigQuery, Fivetran, Stitch, and dbt, among others.

Analytics Engineering Salary 

According to data collected by ZipRecruiter, data analytics engineers earn an average annual salary of approximately $111,480 per year, with the majority of individuals holding the title earning between $85,000 and $110,000 annually.

That being said, the exact salary that you can expect to earn will depend on a number of factors, including the size of your employer, your location, the industry you operate within, and how much experience you have in the field. With the right set of circumstances, a small percentage of analytics engineers have the potential to earn a salary as high as $203,000 per year.

Developing the Right Skill Set to Break Into the Field

If you’re considering a career as an analytics engineer, then it’s critical that you begin working now to develop the skills you will need for success.

As a result of the unique position that the analytics engineer holds on the data team between the data engineer and the data analyst, an analytics engineer must be skilled in multiple domains. Strong software engineering and technical skills will help you communicate and work with the data engineers on your team; strong analytical skills will help you to work with the analysts on your team; and a general understanding of business will empower you to communicate with everyone else in your organization who may interact with the data team.

Many analytics engineers find that they fall into the career after working on a data team in a different capacity for a number of years, picking up the necessary skills over time that enable them to perform the duties. A quicker, more efficient path into the field is to earn a degree specifically designed to prepare you for the role, such as a master of science in data analytics engineering.

Not all data analytics engineering programs are created equal, however. For that reason, it’s important to evaluate the curriculum of your program of interest, and confirm that it has been designed to specifically help you develop skills in each of the domains outlined above. If you would like to work within a particular industry, it can also be helpful to find a program that allows you to specialize in that field.

Students who enroll in the Master of Science in Data Analytics Engineering at Northeastern, for example, are required to complete core requirements in order to lay a solid foundation in data engineering, data and statistical analysis, and data visualization. They can then focus their education with courses that apply analytics engineering to a range of fields, including (but certainly not limited to):

• Business administration
• Civil and environmental engineering
• Computer science
• Computer systems engineering
• Data science
• Electrical and computer engineering
• Health informatics
• Public policy and urban affairs

Beyond this, the MS in Analytics Engineering at Northeastern offers students a number of experiential learning opportunities in the forms of co-op placements, which allow students to begin gaining experience applying their lessons on real-world problems faced by businesses and organizations. 

Regardless of how you break into the field, a career as a data analytics engineer will be a rewarding one. If you are interested in pursuing such a career, it’s never too soon to begin developing the skills and expertise that will empower you to perform the job.

Are you interested in pursuing a career in data analytics engineering? Explore our master’s in data analytics engineering and learn more about the curriculum, experiential learning opportunities, and world-class faculty that define the Northeastern experience.