It's hard to see how organizations can get started with advanced analytics without having a formal structure for their data analytics teams. As you build an analytics team, you will need to define appropriate titles for different members of your organization, as well as a promotion plan that allows team members to aspire to roles with greater responsibility. Currently, a few popular job titles currently in use in the industry include data analyst, big data scientist, business analyst, business intelligence analyst, data engineer, data architect, and machine learning engineer.
Aside from ensuring that your team is properly organized according to each employee's function, you also will need to plan for their personality types and desire for career growth. Some team members will always be more technically inclined and they will need to be able to grow into roles that have increasing technical responsibility, without requiring management skills. These subject-matter experts will be great resources for decision makers who need to understand the analytics capabilities of specific analytics tools or data science models. Other team members may have a good affinity for interacting with the rest of the organization, and they will need to eventually have titles such as manager, director, and vice president. Combining the knowledge of subject matter experts with the knowledge of business processes gained by team members with soft skills helps to create the solid business cases that analytics leaders need to continue advocating for their analytics strategy. Some team members might even become interested in moving deeper into roles within the business and outside of the analytics team. Defining this multipath career ladder for your organization will assure that the analytics talent you hire sees the potential for career growth within your organization.