The data science discipline has been around for quite some time now, but you may have noticed the field gaining traction in recent years. This is due to a number of factors: technology is rapidly developing, digital adoption is consistently increasing, and the amount of data available is growing in volume and complexity. All these and more have built a foundation that has made decision intelligence vital for organizations today.

With the data science boom also comes an increasing demand for the manpower and expertise required. Because in order to extract value from data, you need the right roles and the right skills—and that means you need more than just the quintessential data scientist.

What does a data science team look like?

Let’s take a look at the different data science roles and responsibilities that powers a typical team.

1. Data engineer

Before anything else, it’s important to have a solid foundation for data management—infrastructures such as databases or large-scale processing systems that keep data flowing and processing. Enters the data engineer, who is responsible for developing, constructing, testing, and maintaining that data architecture. In short, they refine the methods in which you bring in data, which often involves tricky engineering aspects.

2. Data architect

Next, you need to make sure that the data you are working with is useable. While the data engineer is responsible for building and maintaining the data architecture, the data architect takes care of conceptualization and visualization. By asking the question “what kind of data should be delivered?”, the data architect visualizes and creates the blueprints for a data management framework that can integrate, centralize, protect, and maintain data sources.’

3. Database administrator

In order to ensure that the database and data is accessible to those who need it, you’ll need a database administrator. Not only do they take care of granting or revoking access for relevant users, but they’re also responsible for the security, proper functioning, and optimal performance of the database.

4. Data scientist

A number of roles in the data science world are responsible for turning data into actionable insights, and the data scientist is one of them. With responsibilities that often overlap with the data analyst, statistician, and even the machine learning engineer, the data scientist provides a solution to a business challenge by applying data processing techniques, machine learning algorithms, and artificial intelligence to structured and unstructured data-producing actionable insights in the process.