Data Engineer vs Data Scientist: The Key Differences

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

Data engineer vs data scientist has been an ongoing debate. When working with data, there are two considerations: design and implementation.

The design bit is taken care of by the Data Scientists and implementation by Data Engineers. To understand the difference between a data scientist and a data engineer, imagine a data team. This team has been assigned a task to build a functional data model.

Now, as the data scientist designs this data model’s framework and all the algorithms, the data engineer is creating and maintaining collection systems for the entire data used in this model.

Keep that scenario in mind as you continue reading. By the end of this article, you will possess in-depth knowledge of both data engineering and data science.

Understanding Data Engineering and Data Science

Before diving into Data Engineers vs Data Scientists, note that they have some common characteristics. However, there are some distinctions that separate one from the other. To fully understand the difference and decide which is the right career for you – it is essential you know:

What is Data Engineering?

Data engineering revolves around building and maintaining the infrastructure. This infrastructure lets the data scientists access the data and interpret it to perform analysis and develop data models.

Data engineering makes the data more useful and accessible for the consumers of that data. In this case – data scientists. For instance, consider data stored in a rational database and it is managed as tables – a spreadsheet.

Each table contains numerous rows that share the same column count. Now, if there is data regarding a consumer or product, it will be stored across several dozens of tables.

What is a Data Engineer?

Now that you know what data engineering is, it is relatively easier to understand what a data engineer is. Imagine data engineering as a process and data engineer as the one performing and overseeing that process.

Data engineers are responsible to build and maintain system architectures either locally or on the cloud. These serve the purpose of collecting and processing large quantities of data.

The traditional method of becoming a data engineer requires you to earn a Bachelor’s degree in CE (computer engineering), CS (computer science), or a similar field. In addition, you need several years of experience in data analysis, project management, software, or computer engineering.

Don’t fret! Since the world is moving towards the cloud. You can forget about acquiring a degree or years of experience before becoming a data engineer. All you need is a cloud engineer certification from Google, AWS, or Microsoft Azure.

If you are interested in learning more about data engineering and getting certified check out these tailored courses by Skillcurb to secure your chances of passing the exam on your first attempt and landing a job in the cloud:

• Google Cloud Certified – Associate Cloud Engineer.
• Google Cloud Certified – Professional Data Engineer.

What does a Data Engineer do?

Data engineers have the responsibility of routinely maintaining model systems for data to be collected and accessed for use. A specific requirement of this role is to work closely with data scientists to ultimately create an efficient data model workflow.

Moreover, data engineers work with raw data that contains machine, instrument, or human errors. As a data engineer, you need to be capable of recommending and implementing new ways to improve data efficiency, readability, and quality.

What is Data Science?

Data Science is the discipline in which the extraction of workable information insights from raw data collections takes place. After cleaning that data, it can be used to build and train statistical ML (Machine Learning) models.

Since data science requires understanding how everything fits together. You must be proficient in computer programming, machine learning, algorithms, statistics, and linear algebra. 

What is a Data Scientist?

As a Data Scientist, your main objective is to properly organize and analyze the data. This process is often carried out by utilizing software specifically designed for the task.

A data scientist must ensure that the final results of the analysis must have good readability and be easy enough for all stakeholders to understand. In a nutshell, data scientists analyze data and extract information but their roles vary depending on where they are working. That is to say, the information will always vary from company to company.

If you are interested in learning more about data analytics and getting certified check out these courses to secure your chances of passing the exam on your first attempt and landing a job in the cloud:

• Introduction to Cloud Data Analytics with Google BigQuery.
• Introduction to Data Analytics on Microsoft Azure Cloud.
• Introduction to Data Analytics on Amazon AWS Cloud.

What does a Data Scientist do?

Data Scientists are responsible for performing data analysis. Normally, they are provided with preprocessed data that already cleared the first phase of cleaning and manipulation.

They provide this data to complex analytics programs, machinal learning, and statistical methods to create models that are predictive and prescriptive. Obviously, they have to conduct vast research on the industry and business in order to build these models. Therefore, there are instances where a data scientist must explore and examine data to locate hidden patterns.

A data scientist must be aware of distributed computing. This is essential as the data scientist will get data processed by the engineering team but make it readable for the business stakeholders.

Comparison: Data Engineer vs Data Scientist

Data engineering and data science are two distinct yet closely related fields in the realm of data management and analysis.

Both roles require a strong understanding of data and the ability to work with large sets of it, but the focus and responsibilities of each role differ significantly.

That being said, the best way to explore the key differences between data engineers and data scientists, is to compare their job responsibilities, skill sets, and career paths.

1. Job Responsibilities of Data Engineer vs Data Scientist

Data engineers are responsible for designing and maintaining the infrastructure that allows data scientists to access and analyze large sets of data.

This includes tasks such as designing and building data pipelines, integrating data from various sources, and ensuring data quality and security.

Data engineers also work on creating and optimizing data storage systems, such as data lakes and data warehouses, and develop tools that automate data management processes.

On the other hand, data scientists focus on using data to extract insights and make predictions. They work on tasks such as analyzing data, building models and creating visualizations to communicate their findings.

Data scientists also work on experimenting with new techniques and technologies to improve their models and find new ways to extract insights from data. They often collaborate with other teams, such as product managers and engineers, to implement their findings and drive business decisions.

2. Skill Sets of Data Engineer vs Data Scientist

Data engineers and data scientists have different skill sets, although there is some overlap. Data engineers typically have a strong background in programming and a deep understanding of databases and data storage systems.

They are also familiar with technologies such as Apache Hadoop, Apache Spark, and Apache Kafka.

Data scientists, on the other hand, have a stronger background in statistics and machine learning. They are also proficient in programming languages such as Python and R, and have experience working with libraries such as pandas and scikit-learn.

Data scientists also have a good understanding of data visualization tools such as Tableau and Power BI.

3. Career Paths for Data Engineer vs Data Scientist

Data engineers vs data scientists have distinct career paths, but they often work together in the same organization. Data engineers typically start their careers as software engineers or database administrators and then specialize in data engineering.

They can advance to become senior data engineers or lead data engineers, and eventually move into management roles such as data engineering managers or chief data officers.

Data scientists usually start their careers with a master’s or Ph.D. in a quantitative field such as statistics, computer science, or physics.

They can advance to become senior data scientists or lead data scientists, and may eventually move into management roles such as data science managers or chief data officers.

4. Pillars of Data Engineering and Data Science

When you pit data engineer vs data scientist together, both roles require a strong understanding of data, but their skill sets and career paths are different.

Data engineers and data scientists often work together in the same organization and collaborate to drive business decisions.

Here’s a quick overview of data engineering and data science pillars:

Data Engineering Pillars	Data Science Pillars
Data Pipelines	Statistical Tools
Big Data (Storage and Processing)	Machine Learning
Model Extract, Transform, Load (ETL)	Computer Programming
Programming languages	Communication and Team Management
Software-oriented issues	Model Building
Improvement of the organization’s efficiency	Data Visualization
Data accessibility improvement	Linear Algebra and Algorithms

Specific Skill Sets of Data Engineers and Data Scientists

Here’s a quick overview of the difference in skill sets between data engineer vs data scientist:

1. Data Engineer Skills

• Adding data into models
• Technologies and Programming languages (SQL, Hadoop, Python, etc.,)
• Data warehousing
• Building data pipelines (includes management and maintenance)
• Data Architecture
• Soft skills (Communication and collaboration)

2. Data Scientists Skills

• Industry-based specialization (merchandise, finance, healthcare, etc.,)
• Mathematics
• Machine Learning
• Artificial Intelligence and Deep Learning
• Data Platforms (Google, Azure, AWS, etc.,)
• Analytical skills (visualization of data, data mining, risk analysis, etc.,)
• Basic programming (R programming, Java, Python, etc.,)
• Technological proficiencies (TensorFlow, Tableau, PyTorch, etc.,)
• Soft Skills (Decision making)

A Detailed Comparison of Skills: Data Engineer vs Data Scientist

1. Programming Languages

Data engineers typically have a strong background in programming, with proficiency in languages such as Java, Python, and SQL. Java is commonly used in data engineering because of its scalability and performance, making it well-suited for building large-scale data processing systems.

Python is also a popular choice for data engineers because of its wide range of libraries and frameworks, such as Apache Spark and Apache Hadoop.

SQL is a must-know for data engineers as they work a lot with relational databases.

Data scientists, on the other hand, have a stronger background in statistics and machine learning. They are proficient in programming languages such as Python and R, and have experience working with libraries such as pandas and scikit-learn.

Python is the most popular language for data science, as it has a vast ecosystem of libraries and frameworks for data manipulation, visualization, and machine learning.

R is also popular among data scientists for its powerful data manipulation and visualization capabilities.

2. Databases

Data engineers are responsible for designing and maintaining the infrastructure that allows data scientists to access and analyze large sets of data.

This includes tasks such as designing and building data pipelines, integrating data from various sources, and ensuring data quality and security.

Data engineers also work on creating and optimizing data storage systems, such as data lakes and data warehouses, and develop tools that automate data management processes.

Therefore, they are well-versed with various databases like SQL, and NoSQL, big data databases like Hadoop, and data warehousing technologies like Amazon Redshift and Snowflake.

Data scientists, on the other hand, need to focus more on the analysis and modeling of data rather than the storage. They may not be as well-versed in the intricacies of different databases but they are familiar with SQL and can work with databases to extract data for analysis.

3. Tools

Data engineers use a variety of tools to design and implement data pipelines, such as Apache Hadoop, Apache Spark, and Apache Kafka.

These tools are used for distributed data processing, data storage, and data streaming. Data engineers also use tools such as Git and Jenkins to manage their code and automate their processes.

Data scientists use a variety of tools to analyze and visualize data, such as Jupyter Notebook, R Studio, and Tableau.

Jupyter Notebook is a popular tool among data scientists for data exploration, visualization, and prototyping.

R Studio is also a popular tool among data scientists for data visualization and statistical analysis. Tableau is a popular tool for creating interactive visualizations and dashboards.

Data scientists also use machine learning libraries and frameworks such as scikit-learn, TensorFlow, and Keras.

Careers Available to Data Engineers and Data Scientists in 2023

Both, data engineers and data scientists have numerous careers to choose from. In addition, the demand for data engineers and scientists keeps skyrocketing as the disciplines evolve further.

The best career path is to dive into the cloud, here are some job roles that you can pursue as a data engineer or data scientist:

Data Engineer Jobs	Data Scientist Jobs
BI Developer	Application Architect
ETL Developer	Machine Learning Engineer
Data Warehouse Engineer	Database Administrator
Technical Architect	Data Analyst
DevOps Engineer	Statistician

The Verdict: Data Engineer vs Data Scientist

If you merge everything you have learned above, the difference between a data engineer and a data scientist is – Data Engineer designs, builds, manages, maintains, and optimizes the infrastructure and processes the data to make it accessible and analysis-ready for the data scientists.

The data scientist takes it up from there to explore information and insights from large data volumes to meet business needs and goals.

Regardless of the path you choose, there are plenty of career opportunities available for both. We have evolved past the times when you would spend a decade getting degrees and experience to land an entry-level job.

Thanks to the cloud, you can easily secure a high-paying job with a Google, AWS, or Microsoft Azure certification. If you are preparing and using the courses from Skillcurb, be sure to take the simulated exams to speed up your process of getting a job as a data engineer or data scientist.

Frequently Asked Questions