Roles in Business Intelligence and Data Analytics Team

Most data science projects, especially the ones of sufficient scale and complexity, are carried out by teams that consists of roles of different skill sets. Different organizations give different names to these roles. However, in most cases, these roles boil down to three broad categories that broadly correspond
to the steps of the analytics workflow.

Data engineers: are primarily responsible for the collection, transformation, and organization of data that is required for an analytics project.

Their skill set grows upon computer science and software engineering, and includes programming, data modeling, and database skills.

Data scientists: also known as statisticians, are responsible for developing and executing the analytical models that process data to derive the desired insights.

Such individuals are well-versed in statistical analysis, and increasingly, in the newer spectrum of powerful analytics techniques, such as machine learning, natural language processing, and social network analysis.

Data translators: are the interface between the analytics team and the rest of the business.

They understand the business side very well and have a broad, though not necessarily very deep, understanding of the technical steps of the analytics process.

They are responsible for defining the business questions that motivate the project, helping translate these into data questions, and developing a strategy for obtaining the right data.

At the other end, they are responsible for translating the results of the analysis into business insights and helping the business stakeholders use these insights to make decisions.

Manager/Leader: Analytics teams usually also include the manager role, who oversees the entire process and manages the relationships among the team members, as well as with the business stakeholders.

Data analysts: are typically more junior employees who help people from across an organization
answer relatively straightforward business questions by creating databases and data warehouses, and using business intelligence and data visualization software to construct charts and reports.