Careers within the field of data science have in recent years seen soaring demand, with the Bureau of Labor Statistics forecasting a 22% increase in job growth from 2020-2030—much higher than the average growth of other occupations. As companies continue to focus on generating, collecting, and analyzing big data to help them run their businesses, this demand shows no signs of slowing.
The following guide offers insight into the key differences between two of the more prominent professions within data science — data scientist and data engineer — and covers everything you need to know to make an informed decision about the career that’s best for you, from roles and responsibilities to average salaries, education requirements, and the various paths that can lead to a dream job working with data.
Is There a Difference Between a Data Engineer and a Data Scientist?
There was a time when data scientists were expected to perform the role of data engineers. But as the field of data has grown and evolved, with data gathering and management becoming more complex and unwieldy, and organizations expecting more answers and insights from the data gathered, the job has been split into two.
Today, the main difference between these two data professionals is that data engineers build and maintain the systems and structures that store, extract, and organize data, while data scientists analyze that data to predict trends, glean business insights, and answer questions that are relevant to the organization.
Data Engineer vs. Data Scientist
Although there is overlap in the skills between data engineers and data scientists, and in the past data scientists were expected to perform some of the duties of data engineers, the two roles are distinctly separate and different.
Role and Responsibilities
It helps to think of data engineers and data scientists as having complementary roles. Data engineers build and optimize the systems that allow data scientists to do their job. Data scientists, meanwhile, find meaning in the troves of data that data engineers manage.
What Does a Data Engineer Do?
A data engineer is a data professional who prepares the data infrastructure for analysis. They are focused on the production readiness of raw data and elements such as formats, resilience, scaling, data storage, and security. Data engineers are tasked with designing, building, testing, integrating, managing, and optimizing data from a variety of sources. They also build the infrastructure and architectures that enable data generation.
Their primary focus is to build free-flowing data pipelines by combining a variety of big data technologies that enable real-time analytics. Data engineers also write complex queries to ensure that data is easily accessible.
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What Does a Data Scientist Do?
Data scientists concentrate on finding new insights from the data that was prepared for them by data engineers. As part of their job, they conduct online experiments, develop hypotheses, and use their knowledge of statistics, data analytics, data visualization, and machine learning algorithms to identify trends and create forecasts for the business.
They also engage with business leaders to understand their specific needs and present complex findings, both verbally and visually, in a manner that can be followed by a general business audience.
Education and Requirements
Many data engineers and data scientists hold a bachelor’s degree in computer science or a related field such as mathematics, statistics, economics, or information technology. And while employers often look for candidates with advanced degrees, it is possible to land a role in data science or data engineering without a degree.
What Are the Requirements To Become a Data Engineer?
Data engineers usually hail from a software engineering background and are proficient in programming languages like Java, Python, SQL, and Scala. Alternatively, they might have a degree in mathematics or statistics that helps them apply different analytical approaches to solve business problems.
To get hired as a data engineer, most companies look for candidates with a bachelor’s degree in computer science, applied math, or information technology. Candidates may also be required to have a few data engineering certifications, like Google’s Professional Data Engineer or IBM Certified Data Engineer. It also helps if they are experienced in building big data warehouses that can run some Extract, Transform, and Load, or ETL, on top of big data sets.
What Are the Requirements To Become a Data Scientist?
Data scientists are usually presented with large volumes of data without any particular business problems to solve. In this scenario, the data scientist will be expected to explore the data, formulate the right questions, and present their findings. This makes it essential for data scientists to have a broad knowledge of different techniques in big data infrastructures, data mining, machine learning algorithms, and statistics. As they also have to work with data sets that come in various forms to run their algorithms effectively and efficiently, they also need to be up-to-date with all the latest technologies.
Data scientists are expected to be proficient in programming languages such as SQL, Python, R, and Java, and be familiar with tools such as Hive, Hadoop, Cassandra, and MongoDB.
Data Scientist vs. Data Engineer Salary
For the analytical mind, both positions offer a highly rewarding and lucrative career.
What Does a Data Engineer Earn?
Data engineers’ salaries depend on variables such as the type of role, relevant experience, and where the job is located. According to Glassdoor, the average salary for a data engineer is about $142,000 per year.
What Does a Data Scientist Earn?
Again, what data scientists earn also depends on the type of job, their skills, qualifications, and where it’s located. According to Glassdoor, on average, a data scientist makes about $139,000 per year.
Career Paths
There is no one set path to becoming a data engineer or a data scientist, but below are some of the common ways in which people have navigated the field to get to their dream jobs.
What’s a Typical Career Path for a Data Engineer?
Data engineering is not usually an entry-level role. Because of this, many data engineers get their start in software engineering or business intelligence/systems analytics — roles that give them exposure to the systems and infrastructure that are crucial to the field of data science.
Many data engineers take advantage of roles such as data architect, solutions architect, and database developer to perfect their data engineering skills, develop a deeper knowledge of data processing and cloud computing, and gain experience with ETL and data layers. Some may also work in data analytics to bolster their knowledge of what data analysts and data scientists need before transitioning into data engineering.
What’s a Typical Career Path for a Data Scientist?
Many data scientists get their start in an entry-level data science role, whether through an internship or getting hired as a junior data scientist. These entry-level positions give new data scientists the opportunity to continue developing their technical skills and to work on projects assigned to them before they advance to designing their own experiments and solving more ambitious business problems.
Data analysts commonly pivot into data science roles either by teaching themselves the relevant skills or by enrolling in an online data science course or bootcamp.
Related Read: Data Analyst vs. Data Scientist: Salary, Skills, & Background
Can a Data Engineer Become a Data Scientist (or Vice Versa)?
The short answer is yes, data engineers can become data scientists and vice versa, with some additional training. The overlap in skills — from knowledge of programming languages to working with data pipelines — means that members of both professions are equipped with the foundational knowledge and vocabulary to have a relatively easy career transition. But, given that data engineers have a greater focus on the architecture and infrastructure that supports the work of data scientists, and data scientists are more concerned with developing and testing hypotheses through data, both professions would have to brush up on additional skills before that can make the leap.
Data Scientist vs. Data Engineer: Which Is Best for You?
Despite the overlap in skills between the two professions, data scientists and data engineers have different responsibilities, and the roles may be better suited to certain personality types.
Consider Being a Data Engineer if…
Data engineers deal mostly with the infrastructure and architecture that stores and organizes data. They are strong coders who like learning and using new technologies, enjoy discovering new ways to make software and systems more efficient, and thrive on helping an organization save time and resources. If you’re a tinkerer who’s always looking for ways to improve the things you build, find purpose in creating the supportive tools that help others do their job, and love playing with the latest tools and technologies, then data engineering might be the right career for you.
Consider Being a Data Scientist if…
Data scientists are analytical thinkers who are curious, aren’t afraid of asking questions, and live for putting their hypotheses to the test. Data scientists not only use data to make sense of things that have happened in the past, they also forecast trends and try to understand what might happen in the future. If you enjoy running advanced statistical analyses, writing machine learning algorithms, and using creativity to solve problems, then a career as a data scientist might be right for you.
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