Data Scientist vs Data Engineer: Main Differences

Initially, I thought that data engineering was just a part of data science, but after doing some deep research and starting my data science degree, I realized that it was pretty different. Both data scientists and data engineers use data to find valuable insights and patterns in various activities and behaviors.

Although both roles transform messy and raw data into something meaningful, they work in different ways. For anyone thinking about a career in data, it’s helpful to understand the exact differences between a data engineer and a data scientist, including what they do, how much they earn, and what their job prospects look like.

What Does a Data Engineer Do?

Data engineering is about making tools to collect data. Engineers create these tools so organizations can understand the data they collect. They also look for trends in the data and make it easier to understand. Their jobs include using programming languages, preparing data for predictions, planning systems, improving data quality, and using math to make things better.

However, data engineers don’t just build things; they also look at the data to find patterns. This helps them figure out how to make sense of messy data.

Their job includes:

Using coding languages to work with data.
Getting data ready for analysis.
Planning how systems should work based on what a client needs.
Making sure data is accurate and useful.
Using math and unique methods to make things work better.

Data engineering is like building the foundation for understanding data, so it’s a big deal!

What Does a Data Scientist Do?

Data engineers create the systems to collect data, while data scientists interpret it. Data can be massive and just look like words, numbers, or symbols. Data scientists use their experience to understand these sets of data.

Sometimes, it’s easy to look at data objectively. Other times, we need to come up with ideas based on what the data shows. They use things like predictive modeling and machine learning, which are made possible by the systems data engineers create.

Things data scientists do include:

Making better models for analyzing data.
Helping with predictive modeling.
Talking with other engineers on the team.
Sharing their findings with the people working on the project.
Checking that the data is correct.
Sorting through big sets of data.
Making sure data is correct and reliable.

Data Engineer vs. Data Scientist Career Outlooks

The U.S. Bureau of Labor Statistics (BLS) tracks job growth for data scientists but not for data engineers. However, since these roles work closely together, the growth outlook for data engineers is likely similar to that of data scientists.

Just like any other profession, the economy can affect the number of job openings in data science. However, data engineers and data scientists work in many different industries, so there are plenty of opportunities. The BLS predicts that jobs in data science will grow by 36% from 2021 to 2031, creating around 40,000 new jobs.

Most data science jobs are found in the following areas:

Computer systems design
Company management
Technical consulting
Scientific research
Credit mediation

California has the most data science jobs because of its large population and Silicon Valley. Other states with a lot of jobs in this field include New York, Texas, North Carolina, and Illinois, according to the latest BLS data.

Data science is a challenging field to get into because it’s very competitive. For the past seven years, data scientists have been named the top job in America by the job listings website Glassdoor. This is mainly because data scientists can earn a good salary.

Data Engineer vs. Data Scientist: Salary

Data engineers and data scientists are both well-paid professions. In the United States, the estimated average salary for a Data Engineer is around $137,000 per year. The average salary for a Data Scientist is $121,000 per year.

Where you work can also impact your salary. Washington state offers the highest pay for data science jobs, followed by California, the District of Columbia, Massachusetts, and Maryland.

In terms of specific cities, places like San Jose, the San Francisco Bay Area, Seattle, the District of Columbia area, and the New York tristate area have the highest salaries for data science professionals, according to the BLS.

Key Differences Between Data Scientists and Data Engineers

Should I Become a Data Scientist or Data Engineer?

When deciding between a career as a data scientist or a data engineer, it’s crucial to align your choice with your interests. If you like building systems for collecting and processing data, go for data engineering. However, if you prefer analyzing data to find insights, becoming a data scientist might be a better fit. By considering your passions and strengths, you can make an informed decision that aligns with your career goals and aspirations in the dynamic field of data science.

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