Career Ladders
Candidates and employees often evaluate their growth opportunities when considering which company and mission they want to work for. A startup with a handful of employees for each role does not require much structure in defining roles, responsibilities, and titles. However, when companies start to grow there is a need for standardizing career guidelines. At this point managers and team members get together to create career ladders for specific roles. In this article we describe what career ladders are used for and we provide a framework for developing career ladders for data roles.
What Career Ladders are for
A career ladder is a document that is usually public within a company (and sometimes even outside the company) that clarifies levels, titles, and responsibilities for a role. Given the schematic nature of career ladders, they are typically presented using bullet points or a spreadsheet format. Some of the most important reasons why career ladders are useful include:
Career ladders provide guidance on career progression, and set expectations for what is required for a given role at each level.
They are specific for each role. The career ladder of a Data Engineer is different from the career ladder of a Data Scientist, since their core technical responsibilities are different. The 2 ladders could share common traits when describing non-technical skills.
Career ladders are useful to guarantee and communicate fairness within a team. By increasing transparency on roles, titles, and levels, they set proper expectations within a team, and they reinforce the notion that everybody’s career progress is measured against the same framework.
They are also helpful for setting a common framework across teams, when the same roles are present. This is important, for example, when calibrating performance assessment and discussing promotions.
Hiring managers often discuss career opportunities with candidates. Sharing a career ladder with them is an efficient way to show that the career progression of team members is a priority and that a framework already exists. This will help candidates determine whether the job opportunity is a good fit for them.
They can also be used to calibrate levels upon hiring. For this reason, they could contain requirements in terms of education and past experience for each level.
Managers are supposed to give frequent feedback to their team members. However, without a proper framework in mind, it is hard to structure the conversation in a productive way. Having a well defined career ladder provides a framework for giving feedback. A manager and their report can easily go over the bullet points of a career ladder to discuss recent performance in the areas that define their role.
Career ladders could also describe the process to switch from one role to another (for example from Data Analyst to Data Scientist, or from an individual contributor role to a managerial role).
What Career Ladders are NOT for
Career ladders are not a checklist of tasks that team members need to perform one at a time in order to progress to the next level as quickly as possible.
These documents provide an indication of the common skills and responsibilities that a certain combination of role and level typically requires. However, there should be enough room for employees to specialize in certain skills, and bring new ones to the table if required by the business. Career ladders are not an exhaustive list of skills and responsibilities.
A career ladder is only useful if it is used by managers and team members (for example in interviews, in feedback sessions, when calibrating across teams, during promotions). If a career ladder document is well polished but over-engineered and not used by the team, it is time to change it.
How to Build a Career Ladder for a Data Role
First, think about the different levels and titles associated with the role under consideration. For example, levels and titles might look like this for a Data Scientist role:
L3 - Data Scientist I
L4 - Data Scientist II
L5 - Senior Data Scientist I
L6 - Senior Data Scientist II
L7 - Staff Data Scientist
L8 - Principal Data Scientist
…
Then, consider the broader categories of skills for the role. For example for a Data role one could consider:
Technical skills that define the role (e.g. coding, analytics, experimentation, Machine Learning, etc.)
Scope & Impact. This category could contain a description of non-technical skills like the ability to deliver effective presentations, influence others’ roles, work with stakeholders, take on projects that have a large scope and affect key business metrics.
Team Building. This bucket can contain skills that indirectly influence the business, but directly affect the operations of a team, such as the ability to mentor more junior team members, interview candidates, and facilitate meetings and team processes.
Within each category, and for each combination of role and level, think about the required proficiency for the defining skills, for example:
For an entry level Data Analyst, technical skills could include knowledge of basic SQL commands and the ability to summarize and present common summary metrics. For a Senior Machine Learning Engineer there could be more emphasis on the development of statistical models and their deployment.
Scope & Impact can be tailored to the type of projects that different roles work on. For example, the scope of a Data Analyst will grow by working on larger projects and presenting analytical insights to a broader or more senior audience, while a Data Scientist or a Machine Learning Engineer should work on increasingly complex research projects and associated data systems that directly impact key business metrics.
Team building skills and opportunities should also grow with seniority, and can be measured by the number of team members that are positively affected by one’s influence.
Finally, it is time to put everything together and establish a formal progression within the role, across levels and titles. For example, a slice of your career ladder for Data Scientists might look like this:
…
L4 - Data Scientist II
Technical Skills
Coding: <insert skills here>
Analytics: <insert skills here>
Scope & Impact
Scope: <insert expected tasks here>
Impact: <insert expected impact here>
Stakeholder Management: <insert expected influence here>
Team Building
Mentoring: <insert expected influence here>
Leadership: <insert expected influence here>
Processes: <insert expected influence here>
L5 - Senior Data Scientist I
Technical Skills
Coding: <insert skills here>
Stats: <insert skills here>
Scope & Impact
Scope: <insert expected tasks here>
Impact: <insert expected impact here>
Stakeholder Management: <insert expected influence here>
Team Building
Mentoring: <insert expected influence here>
Leadership: <insert expected influence here>
Processes: <insert expected influence here>
…
Additional Remarks
Career ladders should be actively developed and reviewed periodically, for instance as part of an yearly review process. No career ladder is perfect, and adapting documents to the needs of a team and a business is better than chasing perfection.
Although changes are expected, one should be careful about modifying levels and titles. This can create negative dynamics between existing team members and new joiners, and a sense that the promise of fairness that the document embodies might have been broken.
If a company uses Performance Improvement Plans (PIP) for employees that don’t reach the next level within a certain amount of time, this information should be included in career ladders for transparency.
Many companies have “terminal levels”, after which there is no pressure for continued progression, assuming good performance at the terminal level. For example, a career ladder for a Data Scientist could specify that the “L5 - Senior DS” level is a terminal level: although it is possible to achieve a promotion to L6, that is not expected within any time frame.
An FAQ section in your career ladder is helpful to clarify doubts, anticipate questions.
Are you trying to design career ladders to attract and develop the right talent for your organization? Data Captains can help you. Get in touch with us at info@datacaptains.com or schedule a free exploratory call.