These days, the business world runs entirely on data and none of the companies can survive without data-driven strategic plans and decision making. The field of data science is quite broad and contains a significant number of job positions including data scientist and data engineer. If you want to step into the data science field, it’s crucial to understand the differences between a data scientist and data engineer to identify whether it’d be possible for you switch positions without investing much effort and time.
In this post, we’ve tried to outline the key differences between these two positions to help you make an informed decision. Let’s start the discussion.
1- What’s a data scientist?
Data scientists are the people who’ve got the ability to derive actionable insights from massive datasets to address specific business problems. At their core, these people analyze massive amounts of data to develop applied mathematical models.
2- What’s a data engineer?
A data engineer is a professional who focuses on preparing the data infrastructure for analysis. Their job responsibilities encompass the production readiness of data and various other things like resilience, scaling, formats, and security.
3- Skills required for each position
At their core, data engineers come from a programming background. This usually encompasses Python, Java, or Scala. These people usually have an emphasis on big data and distributed systems.
Data scientists, on the other hand, usually come from a statistics and/or applied mathematics background together with computer science. These people also need to interact with different business domain experts to cultivate the desired insights.
4- Overlapping skills between a data scientist and a data engineer
There’re various skills where both of these positions’ abilities overlap. For instance, both of them overlap on programming. However, a data scientist’s are usually well behind that of a data engineer. They also overlap on analysis. Here, a data scientist’s analytics skills are well beyond the analytics skills of a data engineer. Probably the biggest overlap can be observed when it comes to big data. A data engineer uses his/her systems creation and programming skills to develop big data pipelines. And a data scientist uses his/her advanced math and limited programming skills to develop advanced data products utilizing those existing data pipelines.
At some organizations, data scientists are tasked with doing things that data engineers should. While data scientists aren’t equipped with the skills to become data engineers, they can acquire the skills. On the other hand, it’s far less common when data engineers begin doing data science. In reality, these positions aren’t interchangeable and it may not be completely easy for a data engineer to become a data scientist. However, recently we’re seeing a new breed of engineers who’re proficient in both data science and data engineering. These people have enough experience and knowledge to work in both fields. These people are called machine learning engineers who’re cross-trained to become experts at both fields. As the bar for performing data science is decreasing gradually, we can expect to see the value of these people increasing only.
