A career ladder is a document that clarifies levels, titles, and responsibilities for a given role. It is useful for setting expectations, comparing performance across team members and teams, and providing a roadmap for career progression. In this article we provide a framework for developing career ladders for data roles.

The job market for data roles is growing and evolving at a rapid rate. However, the hiring process remains stressful for both candidates and interviewers. In this post, we reflect on a few generic principles of interview processes, and some specific tips for data roles. The hiring process can benefit from more transparency, coordination, and access to information for all parties involved.

There are many data roles. We describe 4 archetypes: Data Engineers, Data Analysts, Data Scientists, Machine Learning Engineers.

The focus should be on project areas and responsibilities: this means investing in data infrastructure first. Once the data is collected and organized, insights can be more easily extracted to inform business decisions. A structured approach to managing data also enables the development of Machine Learning solutions that can automate business processes and create customer facing services.