Companies in numerous sectors, including banking, healthcare, and transportation, are beginning to incorporate data science into their daily operations. An ideal and effective data science team must be able to handle a high volume of work with relative ease.

Even so, it’s crucial for businesses to build a workforce that can effectively manage AI duties in order to meet any data-related issues they may face.

Even though research over the past few years has established that data engineering is an essential step towards Data Science and AI, many companies still ignore, misinterpret, or skip this step altogether.

Due to a lack of resources and expertise, many Data Scientists end up performing the duties of Data Engineers. Companies frequently make the mistake of conflating Data Scientists and Data Engineers, which leads to hiring the wrong individual for the job and causing confusion among the staff.

The question of whether data engineers are necessary for data scientists arises since the two professions share a common purpose but require different skill sets. Let’s investigate these facets to acquire a more complete picture of the situation.

Integration of Knowledge

Both fields require a certain level of interdependence and mutual aid, therefore some skill sets may be transferable between them. In order to make the partnership as smooth as possible, they should take into account the choices and duties of one another.

While familiarity with data engineering is helpful, it is not required for a data scientist to perform analysis on data that has been processed by a data engineer or to work in harmony with another group (including a data engineer) for the benefit of the organisation.

Technical expertise in data engineering fundamentals

To back up their claims, data scientists can sometimes use their familiarity with fundamentals of data engineering.

You may not need to construct a data pipeline, for instance, if you already have a proof of concept.

If you find that you need some grounding in data engineering to help verify the results of your analytics, then by all means, go ahead and get some training in that area; it will only help to clarify your thinking.

Keep in mind that even with little familiarity, a data scientist cannot fulfil the role of a data engineer.

Now we’ll take a look at how data scientists can benefit from a fundamental understanding of engineering.

SQL, or the Structured Query Language (SQL)

Even though SQL has been around for a while, database management solutions remain an integral part of many businesses. There is a need for data scientists to learn the fundamentals of SQL.

Acquiring some hands-on experience with data processing would also be useful.

Data Scientists, on the other hand, receive no payment for doing database maintenance or improving the quality of the data stored in the databases by means of writing SQL reviews.

Data Processing in the Cloud

As services like Google Cloud and Microsoft Azure have proliferated, knowledge of cloud computing has become an invaluable skill for Data Scientists.

These businesses will also encourage their data science teams to make use of their cloud infrastructure while developing new initiatives. On the other hand, it is not the responsibility of a Data Scientist to learn how to construct an entire system or pipeline from scratch.

However, knowing the pipeline and how to put the idea into action on the cloud can be really helpful.

Clusters, virtual machines, and data warehouses are just a few examples of the kinds of infrastructure that can be used in the development and deployment of advanced machine learning systems. As a result, understanding how to administer such software might prove useful.


Data Science and data engineering as a field are ever-changing in order to address new challenges.

While data scientists should be curious about the future, they typically just need a fundamental understanding of the applications and systems mentioned above. Knowing how it works and how it will be configured is usually sufficient.

However, one must be open to change by educating oneself appropriately.