As the world generates millions of terabytes of data every day, data science and data engineering are among the most in-demand jobs in the tech sector. Although similar in the overall skills required, each path comes with unique responsibilities.
In this article, we will explore the differences between what data scientists and data engineers do and help you choose which career best fits your strengths.
Key takeaways:
- Data scientists turn the collected data into insights and collaborate with other teams to create real-world business solutions.
- Data engineers build the infrastructure that stores, processes, and delivers that data efficiently.
- Industry demand for both jobs is on the rise, and both offer excellent salaries, with data scientists earning slightly more.
What do data scientists do?
Data scientists are responsible for interpreting large volumes of data to uncover patterns, predict trends, and support strategic business decisions.
As a data scientist, you will build and train machine learning models, perform statistical analysis, and create data visualisations while collaborating with other departments to turn the data into practical solutions.
In addition to your technological knowledge, you will benefit from creative thinking and storytelling skills when reporting the insights you gathered from your data analysis to marketing and product development teams to achieve your employer’s business goals.
What do data engineers do?
Data engineers, on the other hand, enable data scientists to do their work. They design, build, and maintain the data systems that allow organisations to collect, process, and store massive datasets efficiently.
As a data engineer, you will develop and manage ETL (Extract, Transform, Load) pipelines, integrate data from multiple sources and Application Programming Interface (APIs), and ensure data quality, consistency, and security. You will also optimise databases and cloud storage performance.
Key skills for data scientists
Data scientists’ primary role is to extract meaning from existing data. The most valuable skill in this position is combining technical proficiency with strong analytical thinking and business understanding.
In addition to a solid foundation in Mathematics and statistics, data scientists must master programming languages such as Python, R, and SQL. They must also employ knowledge of machine learning and cloud computing.
As a scientist, you will be responsible for gathering meaning from the collected data. You will identify relevant problems and trends, translate data findings into actionable solutions, and communicate with stakeholders across other departments to turn insights into strategies that advance the company’s goals.
How to become a data scientist
The first step in becoming a data scientist is pursuing a suitable degree. Studying Computer science is a common starting point for both data scientists and engineers. Other possible degrees for data scientists are:
- BA/BS in Statistics and Data Science
- BS in Information Systems (Data Networking)
- BS in Information Technology and Systems
- BS in Physics and Data Science
- BS in Economics with Data Science
Before starting your first job or internship, create a portfolio that showcases your ability to extract insights from data and communicate results clearly. Participate in Kaggle competitions, collaborate on open-source projects and publish your analyses on platforms like GitHub to catch the eye of potential employers.
If you want to stand out in the job market, certifications like the Google Data Analytics Professional Certificate, IBM Data Science Professional Certificate, and AWS Machine Learning Specialty are valuable assets.
Key skills for data engineers
As the builders of the systems that organise large amounts of data, engineers typically have a strong background in software development. For this career, you will need to develop skills centred around writing efficient code, designing and structuring databases, and cleaning data.
Data engineers typically use programming languages and tools that support large-scale data processing, such as SQL, Hadoop, Spark, and AWS.
As a data engineer, you must also understand data security and governance guidelines to ensure that information is secure, compliant, and properly managed. You will need to implement data encryption and access control, maintain data integrity, and comply with GDPR laws.
How to become a data engineer
If you want to become a data engineer, these degrees will provide you with a strong foundation in advanced database management, systems architecture, and cloud infrastructure:
- BS in Computer Engineering
- BS in Computer Science
- BS in Data Analytics and Systems Engineering
- Double degree in Engineering and Information Technology
- ME in Software Engineering
Like data scientists, your resume will also benefit from credentials such as Google Professional Data Engineer, AWS Certified Data Engineer, and Microsoft Azure Data Engineer Associate.
A portfolio that demonstrates your ability to build data pipelines, integrate APIs to collect and process data, and design data lakes can open doors to entry-level positions like junior data engineer or ETL developer.
Salary and job demand: data science vs data engineering
The amount of data generated worldwide is rapidly growing and is expected to reach nearly 400 zettabytes by 2028. Due to this volume, the job outlook for both data scientists and engineers is particularly favourable.
The U.S. Bureau of Labour Statistics projects that data science positions will grow by 34% from 2024 to 2034, and the latest report indicates the median annual salary for data scientists is USD 112,590.
Data engineers will face a slightly lower demand in the upcoming years. However, the median entry-level annual salary averages at USD 109,000, according to Glassdoor, with the earning potential increasing with experience: Lead Data Engineer salaries range from USD 167,000 to USD 248,000.
Which career fits you best?
Both careers offer job stability and high salaries. Choosing the best option for you requires evaluating your technological and business skills.
Choose data engineering if you:
Enjoy coding and building infrastructure for massive data systems.
Master tools like SQL, Hadoop, Spark, and AWS.
Value cybersecurity and data integrity.
Want to work with cutting-edge tools in cloud computing and automation.
Like solving complex challenges, such as optimising pipelines or integrating APIs.
Choose data science if you prefer:
Running analyses and building models.
Working with systems like Python, R, SQL, and Tableau.
Finding patterns and discovering trends.
Using statistics and probability to create real-world solutions.
Communicating your insights to a marketing or product development team.
The key to a fulfilling career in data is to choose the path that best aligns with your interests, strengths, and long-term goals.
