The Big data field is getting bigger and bigger with each passing day, due to which new roles are being created, and existing ones are expanding. One of the most in-demand jobs in the data space right now is of the data engineers and data scientists, but there are still many people out there who don’t have a clear understanding of the difference between the two.
The positions may sound the same – and you may think they’re the same, with similar job descriptions or candidates. But, they’re very different, with less overlap than the names may imply. In this article, we’ll explore what these job titles mean, their requirements, and so forth. So, let’s begin.
Data Engineer vs. Data Scientist: What are they
What is a Data Engineer?
A data engineer can be described as a data professional who designs and builds pipelines that transform and transport data into a compatible format for analysis by Data Scientists and other end users.
Data engineers usually hail from a programming background, possibly as a result of a software engineering degree. Their background is generally in programming languages like Java, Python, and Scala.
Also, they have moved far beyond building apps & simple systems and specializes in distributed systems that can be used to analyze large volumes of data. However, their primary goal is to help data scientists turn oceans of data into valuable and actionable insights.
What Is a Data Scientist?
As mentioned above, a data engineer is responsible for building the infrastructure and cleaning up the data for analysis by data scientists. But before data engineering was created as a separate role, all this work used to be performed by data scientists.
Today, data scientists only concentrate on analyzing the data that was cleaned and prepared for them by data engineers. So it’s safe to say that it’s not really a case of data scientist vs. data engineer. They don’t work against each other. Instead, they work in tandem to help businesses achieve their goals.
Do they overlap?
Certainly, there are overlapping skills concerning programming, but this doesn’t mean that the roles are interchangeable. Data engineers tend to have more advanced programming skills, while data scientists are much better at data analytics.
Most data scientists don’t learn programming as a hobby; instead, they learn it out of necessity, as it is the only way they can conduct more complicated analysis on data sets.
Data engineers don’t need to have advanced analytical skills. They need to be able to understand the requirements of each project.
Data Engineer vs. Data Scientist: Role Requirements
What Are the Requirements for a Data Engineer?
You should have a bachelor’s degree either in Computer Science, Software Engineering, Applied Mathematics, or IT (Information Technology), if you want to get hired as a data engineer. Now, your degree, while important, is only part of the story – getting the proper certifications can be hugely valuable. There are a few data engineering certifications out there, including Google’s Professional Data Engineer or IBM Certified Data Engineer.
Companies also want their candidates to have a large technical skillset, as it helps them creatively approach complex problems. Moreover, you should be experienced in constructing and optimizing data pipelines from scratch.
Every data engineers need to know the following programming languages:
- Python
- Java
- C++
- Scala
- SQL
- JavaScript
- and so on.
Along with these languages, a data engineer’s toolkit may also include systems like Oracle, Hadoop, and MySQL.
What Are the Requirements for a Data Scientist?
When hiring a data scientist, most employers look for candidates with a master’s degree in math, statistics, physics, or a similar type of applies math. Since the demand for data scientists is far more than the supply at the moment, organizations often hire people even without a graduate degree.
Data scientists are usually presented with a huge amount of data without any particular business problems to solve. In such a situation, he/she will be expected to explore the data, formulate the right questions, and present their findings.
Thus data scientists must have a broad knowledge of various techniques in the following:
- Data mining
- Machine learning
- Statistics
As they also have to work with data sets that come in various shapes and sizes to run their algorithms shortly, sweetly, and succinctly, they also need to keep up with all the latest technologies. Hence, it’s important to have basic programming skills and experience with languages and databases (big/small) technologies.
Day-to-day, a data scientist works with systems like MatLab and Rstudio and languages like Python and R.
Data Engineer vs. Data Scientist Salary: How Much Do They Earn?
Well, I don’t think there is much difference between the two here. They both offer a highly rewarding and lucrative career. So, it doesn’t matter which career path you opt for. You can rest assured that there will be a significant demand for your skills and experience.
How Much Does a Data Engineer Make?
The salary of data engineers depends on several different factors, such as relevant experience, where the job is located, and so forth. According to Glassdoor, the average salary for a data engineer is about $102,864/year.
How Much Does a Data Scientist Make?
Again, data scientists’ salary depends on several different factors, such as their skills, qualifications, where it’s located, and so forth. According to Glassdoor, on average, a data scientist makes about $113,309/year.
As mentioned at the very start, big data is on the rise, so as this space continues to expand, you can expect these numbers to rise to reflect demand.
Final Words…
I guess this much information will be enough to get you started, so choose one role and then specialize in it. Either way, both positions have an extremely positive job outlook and are lucrative.
Thank you for reading!