Data Scientist vs Data Engineer: Which Career Path is Right for You? (Updated in 2024)

Data scientists and data engineers are crucial to implementing a good data strategy. These positions may seem similar to an outsider, but in many companies today, the two have significant differences in responsibilities and skill sets.

The job prospects of data experts are promising, with the employment of data scientists expected to grow by 35% between 2022 and 2032. However, there are signs that demand for data engineers may be higher due to the challenges of managing big data and the rise of AI.

Understanding the difference between a data scientist vs data engineer is key in determining which career path is right for you. In this article, we’ll explain the difference between the two, the responsibilities of each expert, salaries, skills, and more to help you choose the more fulfilling role.

Data scientists’ primary responsibility is deriving useful insights from data so organizations can use these insights to make better, data-driven decisions. These decisions help companies improve their operations and gain a competitive advantage.

To derive insights, data scientists must understand the business/industry and build statistical or machine-learning models to help them find trends in the data provided to them.

On the other hand, data engineers ensure that data scientists and others can access clean, high-quality data for analytics, machine learning, visualizations, etc. They create and maintain databases and data processing systems that can handle large volumes of data and present clean data in convenient formats to others. This role has become especially critical in the era of big data.

Here is a more detailed comparison of responsibilities between data scientists and data engineers:

Data Scientists’ Responsibilities

Data scientists are typically charged with generating insights from data and presenting them to the decision-makers. Their responsibilities vary between different organizations but usually include:

• Developing Hypotheses: Conducting industry research and coming up with business questions that can be answered using statistical or machine-learning models.
• Data Definition: Working with domain experts in the organization to define the data needed for statistical analysis or training of machine-learning models.
• Model Selection: Picking the right models to answer specific business questions.
• Model Creation and Training: Creating and training machine learning models to find complex relationships in the data.
• Analytics: Using statistical analysis to identify trends in data and make predictions or identify problems.
• Experimenting: Testing different model iterations to optimize results.
• Communicating Insights: Presenting their findings to relevant stakeholders and creating dashboards to automate the communication of findings.

To handle these responsibilities, data scientists must understand the business, industry, and available data well.

Data Engineer Responsibilities

Unlike data scientists, the roles of data engineers are fairly consistent across different industries. Their responsibilities typically include:

• Developing Architectures: Data engineers design, construct, and maintain the systems used to process and store data, i.e., the data pipeline, and ensure the data is available to others.
• Data Pipeline Testing: As they develop the data pipeline, data engineers must test different parts of the system to ensure they perform as expected.
• Data Collection: Data engineers gather data from different sources, including APIs, data brokers, and other databases.
• Data Management: Data engineers manage all data and metadata stored and ensure data integrity is protected.
• Monitoring Pipeline Stability: The performance of the data pipeline may change over time due to differences in data quality and requirements or changes in the models being used. Data engineers track the performance of the system and ensure it remains stable.

It’s common to find data scientists and data engineers with computer science backgrounds. Both careers require familiarity with certain aspects of software engineering, including programming, database management systems, and cloud computing. However, there are plenty of differences in their educational backgrounds as well.

Data Scientist Education

Data scientists must have a good understanding of statistical and mathematical concepts. They also need some knowledge of software engineering, econometrics, and operations research. Some data scientists are also subject matter experts in fields where data science is widely used, such as insurance, healthcare, manufacturing, and energy.

Many data scientist roles require candidates to have a bachelor’s degree in data science, computer science, or a related field. Other positions require a master’s degree or a PhD. It’s also common for data scientists to have undergraduate degrees in other areas and a master’s degree in data science. Others simply attend a data science boot camp to gain the expertise needed for this career path.

Data Engineer Education

Data engineers usually have a background in computer engineering, computer science, or a related field. They may also have a background in mathematics, statistics, IT, and similar fields but are less likely to have credentials related to the business side of company operations.

Like data scientists, many employers prefer candidates with a master’s degree or a PhD. However, you can still land a data engineering role without a degree if you have the right skills and experience.

Employers looking to hire data scientists will typically list skills such as:

• Analytical and problem-solving skills
• Statistical modeling and machine learning
• Data visualization
• Programming, i.e., Python, SQL, R, etc.
• Communication skills
• Familiarity with big data platforms and big data analytics
• Cloud computing
• Business knowledge/subject-matter expertise

For data engineers, commonly listed skills include:

• Programming skills, e.g., Python, Scala, and Java.
• Databases, e.g., MySQL, MongoDB, and PostgreSQL.
• Familiarity with big data technologies, e.g., Spark, Hadoop, Hive, etc.
• Experience working with ETL tools.
• Cloud computing and distributed systems
• Data architecture and warehousing

The average base salary for data scientists is around $123,000. The average total compensation is approximately $162,077. The lowest-paid data scientists earn a base salary of around $60,000; the highest-paid earn over $300,000.

On the other hand, the average base salary for data engineers is approximately $107,000, with an average total compensation of $133,017. The lowest-paid engineers command a base salary of about $52,000, while the highest-paid earn over $210,000.

To learn more about compensation for data roles and other positions, visit the salaries page on Interview Query. You can also check out the Jobs Board to see how much employers are currently offering for data scientist and data engineer positions.

Whether you’re fresh out of high school or ten years into a different career, you can start on the journey to becoming a data scientist or a data engineer today. However, you’ll need certain skills to get a job in these roles.

There are undergraduate courses, graduate courses, and boot camps that you can sign up for to gain these skills. Internships, mentorships, and other opportunities can also give you vital experience to get you started. Starting data science or data engineering projects also help boost your CVs!

To find out more about how to start your data science career, check out this article on how to become a data scientist. You can also find out more about the data engineer’s career path by reading this article on Interview Query.

If you already have the skills and education, Interview Query’s data scientist interview questions and data engineer interview questions allow you to take on the types of questions you may be asked during your interview. You can also get coaching from experts who’ve worked at top companies, including Spotify and Amazon, to help you ace that upcoming interview.

As data becomes central to the operations of more companies, the demand for data scientists and data engineers is likely to stay high. For those considering a new data role, choosing between these two career paths can be difficult without taking a closer look.

The insights data scientists provide inform the daily decisions key stakeholders make. However, data engineers ensure that everyone else, including data scientists, has access to the necessary data and that the integrity of the data is not compromised. Both career paths can be fulfilling and, on average, come with six-figure compensation packages.

At Interview Query, we aim to support your transition into any data role. We have resources to help you prepare for interviews for either position and other services to help you make informed career choices. Whether you choose to be a data scientist or data engineer, the future is promising.