What is the difference between Data Scientists, Engineers, and Analysts?

This is an excellent question as the distinction between data engineers, data scientists, and data analysts is often a source of confusion. Many people who are not aware of the distinction between data scientists, data engineers, and data analysts often are in the notion that all three titles are the same. But, they are not!

Let's start by defining the data engineer's role since it's the most distinct one amongst the three. In any data-driven organization, data scientists and analysts need to have trusted, timely and efficient access to data to do their best work. This is where the data engineer comes in, as they are responsible for getting the right data in the right people's hands. They create and maintain the infrastructure and data pipelines that take terabytes of raw data coming from different sources into one centralized location with clean, relevant data for the organization.

For example, in the case of a ride-sharing application, a data engineer would extract raw data from the application database, transform it into analysis-ready data, and load it into a database to be used by data analysts and data scientists.

The distinction between data analysts and data scientists is where it gets a bit murkier.

Data scientists investigate, extract, and report meaningful insights into the organization’s data. They communicate these insights to non-technical stakeholders. Also, they deeply understand the machine learning workflow and can spot its applications throughout the organization. They work almost exclusively with coding tools, conduct analysis, and often work with big data tools. However, data scientists and data analysts' biggest differentiator is that data scientists are tasked with building data products. These could be dashboards to be accessed within the organization, machine learning models that automate a business process, or other related data products.

On the other hand, data analysts do what's described in their job title: analyze data. They are responsible for analyzing data and reporting insights from their analysis. They use a combination of coding and non-coding tools and report their insights to drive the business agenda.

Giving the ride-sharing application example again, a data scientist would be tasked with developing a predictive model to predict the supply of drivers needed at any given moment to allocate incentive planning. In contrast, a data analyst would be tasked with analyzing historical ride-sharing data to answer critical business questions.

However, as data roles mature, it's important to note that there is even more distinction than the data engineer, data scientist, and data analyst paradigm, especially since job titles are often treated as "umbrella terms".

Larger organizations can have these more distinct roles, but in smaller organizations, the data scientist and engineer are more blurred - if not the same person. Data scientists often pre-engineer prototypes and identify data sources and paths, and engineers turn these mechanisms into scalable, operational flows.

I will give it a try with some real-life examples managing data lab projects, to be transparent, I have not come across the term data engineer as much, in the old days, information engineering or data engineering was a thing, and that refers more to the current role of data architects, modelers, etc…

Say we are tasked to build a predictive model, the data scientist is engaged to understand what the model/insight is required and propose the hypothesis, then the scientist will give the requirement of what kind of data will be needed to feed the model, then the data analyst will have the job of finding out where that data exists in the organization or find alternate sources, then the data engineer will have the job of moving the data from the source, e.g. a data warehouse, via some ingestion mechanism to something like a data lake where the data scientist can run the model, in this context, the data engineer’s role is more like a developer that configures or build the ingestion pipeline

In summary, the data scientist builds the model, the data analyst finds the data, the data engineer moves the data.

In this white paper, we outline 8 different types of data roles you can find in any data-driven organization, from data consumers to machine learning scientists, and naturally, to data scientists and analysts, and more!