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5 Key Differences Between Data Scientists and Data Engineers

Distinguish the two building blocks of data science

Photo by Fabian Kühne on Unsplash
Photo by Fabian Kühne on Unsplash

Data Science is an interdisciplinary field that still continues its evolution. The ultimate goal is clear which is creating value out of data. The value can be in various forms such as improved business operations, predictive maintenance, and so on.

Although big tech companies have different positions that are distinctively separated, most companies have vague definitions of what is expected from a data scientist. More importantly, some companies cannot afford to have separate data scientist and data engineer positions which, as a result, generates a position called full-stack data scientists.

In this article, I will try to distinguish the role of data scientists and data engineers. The following key differences will be focused on what they typically do as well as what is expected from them.

Before I start, I would like to share my personal opinion on these two positions. It is hard to compare them according to the difficulty level because the required skills are very different. I think the demand for data engineers is higher than the demand for data scientists. It will make more sense as we go through the differences listed below.


1. Data scientist is the customer of data engineer

Data engineers are responsible for creating robust, resilient, and accurate systems to provide data for data scientists or analysts. Hence, data scientists can be considered as customers of data engineers.

In order to reach the ultimate goal of creating value using data, both parties need to collaborate. Without proper data, a data scientist does not have anything to work on. On the other hand, having well-structured data warehouses or databases will not be enough without analyzing the data.


2. Data engineer is responsible for the raw material

I would like to elaborate on this by using an analogy. Consider a relatively large and popular restaurant. They serve delicious meals thanks to their creative and talented chef.

The talent of the chef is useless without proper raw material to cook. Someone needs to provide the raw material and arrange them in the kitchen so that the chef can access them easily. If all the raw materials are dumped in the kitchen, it would be exhausting for the chef to find what she needs. Besides, the raw materials should be kept properly so that they don’t become distorted.

The data engineer is like the person who is responsible for providing the raw material for the chef. Data is the raw material for the data engineer.


3. Data scientist prepares the meal

Following up on the analogy in the previous step, the data scientist is the chef. She knows how to use the raw material to create value which is in the form of delicious dishes.

The chef sometimes cooks according to a recipe but she also keeps trying to cook her own original dishes. In order to be a successful chef, she needs to know the raw materials very well.

It is of great importance for the chef to have raw materials organized, well-structured, and easily accessible. Similarly, the success of data scientists depends on the quality and accessibility of data. Thus, it is imperative to have data engineers and data scientists working collaboratively to create valuable products.


Photo by Chris Ried on Unsplash
Photo by Chris Ried on Unsplash

4. Data engineering is more practical

It is one thing to design and create a data warehouse or a database. More important than that is to make them efficient, scalable, durable, and fast. Thus, there is a lot of practical work for data engineers.

The tech stack for data engineers contains many tools. Amazon RedShift, Google BigQuery, Hadoop, Spark, Kafka, SQL and NoSQL databases, GraphQL, Airflow, Kafka, Python, and Scala are some of the tools used by data engineers.

Data engineers use these tools to engineer a system to provide a data pipeline. They usually handle an enormous amount of data so the data pipeline vigorous, stable, and scalable. Last but not least, the data needs to be accessed fast. It is obvious that a system that can handle such operations requires lots of practical work.


5. Data scientists spend more time thinking

The work of data scientists is typically much less practical than the work of data engineers. Data scientists analyze the data to drive results or reach conclusions.

They spend more time thinking and researching. Data scientists might be doing less practical work but it does not mean that they spend less time on their tasks. It is sometimes quite difficult to explore the relationship between variables or find the underlying patterns in the data. They should also possess a decent level of knowledge in math and statistics.

The skills of data scientists are kind of soft skills and hard to measure or evaluate. For instance, in order to be a successful data scientist, one should have an analytical mind and think beyond what is obvious. Domain knowledge is also crucial to have in certain areas.

The tech stack used by data scientists is relatively small compared to the data engineers’ tech stack. There are many options to use for the practical tasks of data scientists but having a decent selection will be more than enough. For instance, Python, Pandas (data analysis), Scikit-learn and TensorFlow (Machine Learning), and Matplotlib (data visualization) constitute a proper set of tech stack for a data scientist.


Conclusion

Although we have clearly distinguished the tasks for data scientists and data engineers, it is typical to combine them in one position, especially if you work at a small or medium-sized company.

It is extremely difficult to have expertise in both areas as the number of tools keeps increasing. However, it is worth mentioning that data scientist positions tend towards being full-stack. A data scientist should at least know how to query a SQL or NoSQL database.

Thank you for reading. Please let me know if you have any feedback.


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