Data Science vs. Data Engineering:
What You Need to Know

According to The Economist, “the world’s most valuable resource is no longer oil, but data.”

Despite the value of enterprise data, much has been written about the so-called “data science shortage”: the supposed lack of professionals with knowledge of how to use and manipulate big data. A 2018 study by LinkedIn estimated that there were more than 151,000 unfilled jobs in the U.S. requiring data science skills.

This means that, if you’re interested in working with big data, there are plenty of opportunities. But first, it would help to learn some of the field’s most important terminology. Take the terms data science and data engineering, for example. Although they’re often used synonymously, they aren’t quite the same thing.

What’s the difference between data scientists and data engineers, and what does it mean for you? In this article, we’ll answer the question of data scientist vs. data engineer in terms of responsibilities, skills, salary, and more. 

What is a Data Scientist?

A data scientist is a person who uses statistical tools and methods, in particular artificial intelligence and machine learning, to analyze big data. Whether it’s helping determine which ads to show you on Facebook, or advising Netflix about which films and TV series to recommend, the work of data scientists is essential to modern technology companies.

Data scientists often have a background in mathematics, statistics, machine learning, economics, or some other quantitative field that informs their work in data science. For example, mathematical techniques such as Bayesian inference, linear regression, and probability models are essential to the field of data science.

What is a Data Engineer?

A data engineer is a person who is responsible for setting up and maintaining the data infrastructure and architecture that support an organization’s IT systems and environments. Data engineers must be familiar with fields including programming, data storage, database administration, and system implementation.

The domain of data engineering actually encompasses several related subfields and roles, including:

• Data architects: Data architects are tasked with designing a data management system or systems. The difference between data architects and data engineers is like the difference between architects and engineers for physical buildings: architects focus on conceptualizing data frameworks, while engineers focus on the aspects of implementation.
• Database administrators: Database administrators (DBAs) are responsible for designing and maintaining an organization’s databases, ensuring that they run smoothly and that the data is consistently available for all in the organization who need access to it.
• Data engineers: Data engineers work closely with data architects and DBAs to accomplish their work of building a robust, mature data infrastructure for the entire organization. 

Data Scientist vs. Data Engineer: What’s the Difference?

Like the difference between scientists and engineers of all kinds, the difference between data scientists and data engineers can be summed up as the difference between theory and practice.

To illustrate this concept, let’s suppose that we want to solve a business problem by using big data. For example, we notice that our e-commerce customers are making too many returns on their purchases. We want to address this issue by breaking down all the data we have available on our customers and finding which factors are most likely to play a role: e.g. the items they bought, how much they spent, their geographical location, the time of day of the purchase, etc.

There are essentially two parts to the process.

1. Deciding which data to use and how to handle it
2. Choosing which model to use and how to evaluate it.

In other words, it’s a question of implementation vs. design: data scientists are responsible for designing the analytical framework that underpins the problem, while data engineers are responsible for implementing it and ensuring that it runs smoothly in production.

In the example above, data engineers would be responsible for ensuring that all of the necessary data on customer returns is made available to data scientists for uptake. Data scientists then decide which machine learning models to use to process the data, and analyze the results of each model for insights.

Responsibilities

Although there’s a good deal of overlap in many cases, both data scientists and data engineers have separate yet complementary responsibilities to support their big data work.

The responsibilities of a data scientist may include:

• Identifying the right business questions to ask and the best models to help answer them.
• Cleaning, “massaging,” and preparing data for input into machine learning models and statistical methods.
• Exploring and analyzing data to discover hidden insights.
• Communicating and collaborating with other data scientists and technical colleagues.
• Delivering insights to key stakeholders, including automating the process in the form of real-time dashboards and reports.

The responsibilities of a data engineer may include:

• Building, deploying and maintaining data pipelines, databases, and data management systems.
• Collaborating with data scientists to identify possible new data pipelines and architectures.
• Analyzing how to improve the organization’s data quality and availability.
• Finding potential sources of new data and bringing them into the business.
• Speaking with non-technical managers, executives, and other key stakeholders in order to understand their needs, and to report on progress and results.

Technical Skills

In order to fulfill the responsibilities outlined above, data scientists and data engineers need to have a solid set of skills. Both data scientists and data engineers are expected to have advanced technical knowledge, including strong programming abilities. However, the exact contents of this technical knowledge may differ between the two roles.

The technical skills of a data scientist may include:

• Mathematics: A solid understanding of mathematics is crucial for data scientists. This includes probability & statistics, multivariate calculus, linear algebra, and perhaps other fields depending on the work.
• AI and machine learning: Artificial intelligence and machine learning models are the theoretical basis of a data scientist’s work. Data scientists must be intimately familiar with these different models and how they might apply in various situations. They must also know how to fine-tune the models to get better performance, and how to compare the results of these models in order to select the best option.
• Computer programming: As with data engineers, data scientists must be familiar with computer programming. In particular, data scientists may specialize in one or more big data libraries or frameworks, such as Python’s scikit-learn for general machine learning, OpenCV for computer vision, or PyTorch for deep neural networks.
• Analytics and visualization: Data scientists must be able to examine the results of their analyses to understand how successful each experiment was. Visualization and reporting are key components here, both for one’s own understanding as well as for communicating with other people in the organization.

The technical skills of a data engineer may include:

• Computer programming: Excellent programming skills are a must-have for any data engineer. Some of the most common programming languages for big data include Java, Python, R, and Scala.
• Distributed systems: Big data can be truly massive; one study estimates that the average company manages 162.9 terabytes of data. To deal with this complexity, data engineers need to be familiar with distributed systems, i.e. systems that consist of multiple machines in order to increase their processing power and improve their availability.
• ETL (extract, transform, load) and data integration: ETL is the process of extracting data from multiple source locations and uniting it within a centralized location such as a data warehouse for further treatment and analysis. Data engineers must have extensive knowledge of the ETL process (and variants such as ELT, if necessary). 
• Databases, data warehouses, and data lakes: Data engineers should be familiar with both relational and non-relational database systems, as well as data warehouses and data lakes (repositories of unstructured data). Knowledge of the database query language SQL is essential for data engineers, as well as technologies such as HTTP, SOAP, and REST that help connect these different systems.

Salary

Data scientists and data engineers are both white-collar knowledge workers, which helps them earn an above-average salary. Like most other jobs, of course, data scientist and data engineer salaries depend on factors such as education level, location, experience, industry, and company size and reputation.

According to the U.S. Bureau of Labor Statistics, the average salary for a data scientist is $100,560. A 2020 study by executive recruiting firm Burtch Works found the following median salaries for data scientists:

• Entry-level: $95,500
• Mid-level: $130,000
• Experienced: $165,000

Data engineer salaries are at a similar level, depending on which source you use. According to Glassdoor.com, the national average base pay for a data engineer is roughly $102,000, with a high of $158,000. Meanwhile, according to the University of Wisconsin, the salary of a junior or generalist data engineer can range from $70,000 to $115,000, while the salary of a data engineer with domain expertise can range from $100,000 to $165,000.

So which one pays more—data science or data engineering? It’s tough to say since the available figures appear largely similar. There may be a great deal of variance in these studies, based on who exactly was polled and what their jobs and situations are. In the vast majority of cases, advancing in your career will have a much greater impact on your salary as a data scientist or data engineer than any minor inherent differences between the two professions.

How Integrate.io Helps Data Scientists and Data Engineers

As organizations of all sizes and industries become hungrier for data-driven insights, data scientists and engineers have a great deal of work on their plate. So how can data engineers and data scientists use technology to their advantage by automating much of the underlying big data processes? 

By using Integrate.io.

Integrate.io is a powerful yet user-friendly data integration platform for building robust pipelines from your data sources to a cloud data warehouse, helping both data scientists and data engineers in their work. With its simple, drag-and-drop interface, the Integrate.io platform makes it easy for any user, technical or non-technical, to run sophisticated ETL and data integration workloads.

Want to give Integrate.io a try for yourself? Get in touch with our team of big data experts for a chat about your business needs and objectives and start your free 14-day trial of the Integrate.io platform.