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Data Engineer vs Data Scientist

In a company, it’s extremely important that everyone works together to contribute to constantly bettering the company and customer experiences. A team of data engineers, data scientists and business analysts can work together to extract meaningful insights from raw data that is collected over time. These insights can provide a company with a competitive advantage and assistance in making informed business decisions. Overtime misconceptions have arisen around the roles that data scientists and data engineers play in industry. In many companies, both roles are thought to be interchangeable, however, they are distinctly different. Let’s have a look at the differences!

Data Scientist

Your data scientist is typically involved in extracting insights from raw data that has been cleaned and used to build a machine learning (ML) model. Some of the core skills of a data scientist lies in performing advanced mathematics and statistical analysis on datasets. The data scientists in your company are required to communicate the business value of their findings to non-technical stakeholders, which can be done visually using graphs or dashboards.

Data Engineer

Now your data engineer is involved in maintaining and building the infrastructure required to source raw data, transform it, and store it so that data analysis and ML can take place. Data engineering is the foundation for data science. Your data engineer is not only focused on building the infrastructure required for data science, but  is also responsible for ensuring that the infrastructure is scalable, performs well and provides an end-to-end solution to solve your business problems.

The focus areas of your data scientist and data engineer differ greatly! For example, a data scientist is typically knowledgeable about programming, statistics, operations research and machine learning. Whereas, a data engineer’s knowledge areas typically include big data, data pipelines and model ETL (extract, transfer, load). Model ETL is the process whereby data is pulled from its source, cleaned, aggregated and moved into a new location, such as a data warehouse.

Even though the roles of a data scientist and a data engineer differ, in your company there has to be slight overlap between both teams so that a valuable ML solution can be delivered. If both teams are completely independent, many challenges that could’ve been avoided, can arise. Challenges can stem from different programming languages and environments preferred by the data scientists and the data engineers, which increases the complexity of building a solution. A lack of communication between both teams, can lead to solutions not being scalable, inefficient, or even difficult to work with if the infrastructure is complex. It becomes imperative that your data scientists and engineers are aligned and cross-functional, since their work overlaps continuously when a solution is being developed. Therefore it’s super important that both are on the same page at all times for smooth sailing!

By understanding the differences between the skillset of a data scientist and a data engineer, you will be able to utilise your skillsets appropriately and productively in your business. A collaborative effort between both data scientists and data engineers will allow your company to easily draw the greatest value from your data through building scalable, informative and efficient end-to-end solutions!


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