Data Engineer vs. Data Scientist
Data science jobs have been in great demand in recent years, with the Bureau of Labor Statistics expecting a 22% increase in job growth from 2020 to 2030 — much faster than the typical growth of other occupations. This need shows no signs of decreasing as organizations continue to focus on generating, collecting, and analyzing big data to help them run their businesses.
The following guide explains the key differences between two of the most well-known data science professions — data scientist and data engineer — and covers everything you need to know to make an informed decision about which career is right 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 supposed to be data engineers as well. The role has been divided into two as the field of data has developed and evolved, with data collection and management becoming more complex and unmanageable, and organizations wanting more answers and insights from the data obtained.
Data engineers design and manage the systems and structures that store, retrieve, and organize data, whereas data scientists analyze that data to predict patterns, gain business insights, and answer questions that are relevant to the organization.
Data Engineer vs. Data Scientist
Although data engineers and data scientists have some talents in common, and data scientists were once expected to fulfil some of the functions of data engineers, the two jobs are distinct.
Role and Responsibilities
It’s helpful to conceive of data engineers and data scientists as complementing one other. Data engineers create and improve the systems that let data scientists accomplish their jobs. Meanwhile, data scientists make sense of the massive amounts of data that data engineers manage.
What Does a Data Engineer Do?
A data engineer is a data expert who prepares the infrastructure for data analysis. They’re concentrating on raw data production preparedness as well as features like formats, robustness, scaling, data storage, and security. Data engineers are responsible for the design, development, testing, integration, management, and optimization of data from many sources. They also construct the infrastructure and architectures that allow data to be generated.
Their main goal is to combine a range of big data technologies to create free-flowing data pipelines that enable real-time analytics. Complex queries are also written by data engineers to guarantee that data is easily accessed.
What Does a Data Scientist Do?
Data scientists focus on gaining new insights from the data that data engineers have prepared for them. They conduct online experiments, formulate hypotheses, and uncover trends and forecasts for the organization using their understanding of statistics, data analytics, data visualization, and machine learning algorithms.
They also work with corporate executives to understand their special needs and communicate complex findings in a way that a general business audience can grasp, both verbally and visually.
Education and Requirements
A bachelor’s degree in computer science or a related discipline such as mathematics, statistics, economics, or information technology is required for many data engineers and data scientists. While many businesses prefer people with postgraduate degrees, it is possible to work in data science or data engineering without one.
What Are the Requirements To Become a Data Engineer?
Data engineers are typically software engineers who are fluent in programming languages such as Java, Python, SQL, and Scala. They may also have a degree in mathematics or statistics, which allows them to apply various analytical approaches to commercial challenges.
Most firms search for people with a bachelor’s degree in computer science, applied math, or information technology when hiring data engineers. Some data engineering certifications, such as Google’s Professional Data Engineer or IBM’s Certified Data Engineer, may be required of candidates. It also helps if they have prior experience creating massive data warehouses capable of extracting, transforming, and loading (ETL) large data sets.
What Are the Requirements To Become a Data Scientist?
Data scientists are frequently faced with vast amounts of data and no specific business problems to tackle. The data scientist will be expected to study the data, formulate the appropriate questions, and explain their findings in this scenario. Data scientists must therefore have a thorough understanding of various methodologies in big data infrastructures, data mining, machine learning algorithms, and statistics. They must also be up-to-date with all the latest technologies because they must work with data sets that come in a variety of formats in order to run their algorithms successfully and efficiently.
Data scientists should be comfortable with programming languages like SQL, Python, R, and Java, as well as tools like Hive, Hadoop, Cassandra, and MongoDB.
There is no one-size-fits-all approach to becoming a data engineer or data scientist, but here are some of the most frequent paths people have taken to get to their ideal careers.
What’s a Typical Career Path for a Data Engineer?
Data engineering isn’t typically considered an entry-level position. As a result, many data engineers begin their careers in software engineering or business intelligence/systems analytics, professions that expose them to the systems and infrastructure necessary for data science.
Many data engineers use roles like data architect, solutions architect, and database developer to hone their data engineering skills, obtain a deeper understanding of data processing and cloud computing, and practice with ETL and data layers. Before moving into data engineering, some people may work in data analytics to gain a better understanding of what data analysts and data scientists require.
What’s a Typical Career Path for a Data Scientist?
Many data scientists begin their careers in an entry-level data science position, whether through an internship or a junior data scientist position. These entry-level employment allow young data scientists to hone their technical abilities and work on tasks provided to them before moving on to creating their own experiments and tackling more complex business problems.
Data analysts frequently transition into data science professions via self-teaching data science skills or enrolling in an online course or bootcamp.
Can a Data Engineer Become a Data Scientist (or Vice Versa)?
The short answer is yes, with some further training, data engineers can become data scientists and vice versa. Because of the overlap in abilities — from programming languages to data pipelines — individuals of both professions have the underlying knowledge and terminology to make a reasonably seamless job shift. However, because data engineers are more concerned with the architecture and infrastructure that supports data scientists’ work, and data scientists are more concerned with developing and testing hypotheses using data, both professions would need to brush up on additional skills before making the transition.
Data Scientist vs. Data Engineer: Which Is Best for You?
Despite the fact that the two professions share many abilities, data scientists and data engineers have distinct responsibilities, and the roles may be better suited to different personality types.
Consider Being a Data Engineer if…
Data engineers are primarily concerned with the infrastructure and architecture that is used to store and organize data. They are strong developers that enjoy learning and using new technologies, discovering new methods to make software and systems more efficient, and thriving on saving time and resources for a business. If you’re a tinker who’s always seeking for ways to better the things you make, find meaning in making supportive tools that help others perform their jobs, and enjoy experimenting with new tools and technologies, data engineering could be the appropriate career for you.
Consider Being a Data Scientist if…
Data scientists are analytical thinkers who are curious, don’t mind asking questions, and are eager to test their assumptions. Data scientists utilize data to not only make sense of what has already happened, but also to foresee trends and try to predict what will happen in the future. A career as a data scientist may be perfect for you if you appreciate performing advanced statistical analysis, designing machine learning algorithms, and solving issues creatively.