Data Scientist vs. Data Engineer: How Do They Differ?

Data is the new oil with many sides to its exploration. From the extraction of data down to the system infrastructure needed to contain this data flow, the concept of data organization continues to broaden. For this reason, each complicated role is broken down into different fields.

The two newest and most interesting careers in this niche are data science and data engineering, which are great for those who share an interest in handling data. The tricky part is choosing the best fit for you. This article compares both careers in tech, highlighting their requirements, so you can make the right decision.

What Does a Data Scientist Do?

The first role of a data scientist is to understand a business problem. You can only interpret data after you understand the business problem. A data scientist also gathers raw data—structured and unstructured—from different sources such as web servers, databases, and online repositories.

After that, data preparation follows, which includes cleaning the data you’ve gathered and transforming it into useful data. At this stage, you’ll look for inconsistent data types, missing or duplicate data types, and misspelled attributes.

Data scientists have to remove these errors to get a comprehensive pile of data, which is why data preparation is one of the most complicated parts of being a data scientist. Once data cleaning is done, a data scientist will modify and transform the outcome into readable data that stakeholders can interpret using the best data visualization methods.

You would also employ exploratory data analytics methods to create models and algorithms used in data mining from big data stores. A process that includes defining and refining cleaned data, and selecting features and variables for data mining. Some aspects of data science require programming, so you’ll need to be familiar with basic programming languages.

What Does a Data Engineer Do?

A desktop with codes on it’s screen

The role of a data engineer is pretty straightforward. While a data scientist is responsible for turning raw data into simple and readable forms, data engineers are responsible for building systems that help with these modifications.

A data engineer’s job is to take complex datasets from an application or third-party tool and process them in a way that makes it easy for data analysts and scientists to access and use. Therefore, data engineers focus on building system infrastructures that help pull data, making them ready for use by data scientists.

Data extraction is typically done through data pipelines built by data engineers. One of the ways to pull data is by using API (application programming interface). As a data engineer, your role is to write a series of codes that make an API call that interacts with the server of the sources they are pulling the data from.

This way, data collection begins in a streaming fashion or batch process. It is therefore crucial to understand complex programming languages as a data engineer. The next step in data engineering is to transform the data to fit your data storage.

The main difference between a data scientist and a data engineer is that the former designs the model and algorithm for interpreting raw data, while the latter maintains and creates a system for collecting raw data. A data engineer builds the backbone and infrastructure used in data science.

1. Education

A data scientist needs a bachelor’s degree in data science or a related field to start their career. However, most employers prefer an individual with a master’s degree. A graduate degree can help you stand out.

You may also need to join a data science boot camp to gain some knowledge and experience in this field. A data scientist also needs a deep understanding of data mining, big data infrastructure, statistics, and machine learning algorithms.

On the other hand, a data engineer needs to have a strong background in software engineering and excellent analytical skills from studying applied mathematics, physics, and statistics. For better exposure, you should also join internship programs where you can practice what you have learned.

Unlike becoming a data scientist, you don’t need a master’s degree in data engineering. A bachelor’s degree is sufficient, but you’ll need to take courses in data structure, coding, and database management.

2. Skills

A black screen with the word “skills” written on it

A data scientist needs to hone different skills peculiar to data science. Some of these are data visualization, data wrangling, mathematics, and programming. You need vast knowledge of Python, JavaScript, SQL, and Scala for programming. You’ll need them to create models and algorithms.

Meanwhile, a data engineer needs skills like data analysis, data warehouses, basic machine learning, and knowledge of operating systems. They also need soft skills like communication, critical thinking, and collaboration skills. A data engineer also needs to be skilled in programming languages like Java, Python, C, and C++.

Finally, a data engineer needs to be familiar with Python ETL tools and data-pipeline tools like Fivetran, Talend Open Studio, and IBM DataStage. These ETL tools are very much needed to extract data from various sites.

3. Salary

According to Indeed, the average base salary for a data scientist is $97,678. This salary range can go as high as $188,972, including other cash bonuses, profit shares, tips, or commissions.

Most employers in the US offer 401(k) non-cash benefits in addition to offering insurance, wellness programs, and work-from-home permissions. However, these benefits depend on your employer and your level of experience.

Conversely, data engineers make an average base salary of $112,680, according to Indeed, which can go as high as $218,627 yearly. They can also enjoy privileges like an employee discount, insurance, and non-cash benefits like 401(k) and 401(k) matching. These benefits also depend on your employer, experience level, job role, and qualifications.

4. Experience

A man in a brown suit reading a booklet

You can apply for entry-level roles with at least a year of experience in data science. However, you’ll need to have switched from a related field like information technology to perform well in these roles.

But if you’re starting from scratch, earning a master’s degree and getting relevant experience as a data scientist would earn you better positions. Therefore, to become a full-fledged data scientist, you’ll need around 3-5 years of quality experience working in internship roles and as an entry-level data scientist.

A data engineer also has at least one year of experience to get an entry-level role after a bachelor’s degree in data engineering. However, these roles are usually rare. You can also switch from a data-related role to data engineering. But you’ll need 4-5 years of relevant experience to get better jobs as a data engineer.

5. Career Opportunities

There are rich career opportunities for data scientists based on your experience. Top-rated companies like Meta, Ford Motor Company, and HP employ the expertise of data scientists. They will also find opportunities in health, academia, information, and the government.

A data engineer also has career opportunities that widen according to their experience level. Companies like Netflix, Apple, and Capital need data engineers to assist data scientists. Data engineers work in large companies and in business-related fields. They also fit into academia and information and technology; anywhere that requires data handling.

Choosing the Right Career Path for You

Both careers are rich and solid. They provide maximum exposure and allow you to work with top-rated companies. However, you need to do your homework to find the perfect data-related career. It would also help to write down your interests, so you can choose a career that resonates with your goals.

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