What roles exist in job taxonomy development?

The process of creating a structured map of jobs, titles, skills, and their relationships—commonly referred to as job or skills taxonomy development—is rarely the work of a single individual. It requires a specialized, cross-functional effort because it bridges the gap between the sometimes chaotic reality of job titles used daily and the need for clean, consistent data for analytics and workforce planning. ^[1][9] Understanding which roles populate this development effort reveals much about the rigor required to build a truly effective system, whether it focuses on granular job roles or the underlying skills associated with those roles. ^[3][7]

# Core Architect

The most central figure in any taxonomy project is the Taxonomist or Information Architect. This person is the specialist responsible for the actual structural design. ^[5] Their expertise lies not in the specific subject matter, such as engineering or sales, but in the principles of categorization, hierarchy, and relationship mapping. ^[7]

A dedicated taxonomist understands concepts like facets, attributes, and relationships—the building blocks that turn a flat list of job titles into a usable, navigable structure. ^[1] They need to decide how deep the classification goes. For instance, do they simply list "Data Engineer" or do they create a hierarchy where "Senior Data Engineer" rolls up into "Data Engineering," which in turn belongs to a broader category like "Data & Analytics"? This architectural decision is fundamental. ^[2]

The challenge for the taxonomist is making the structure logical for both human users and machine processing. ^[6] They must translate vague business needs, such as "we need better insight into our emerging tech roles," into concrete, queryable structures. This often involves comparing existing internal structures with industry-standard models or external market data taxonomies. ^[1][6] In securing this kind of work, possessing demonstrated experience in information science, library science, or applied linguistics often provides a strong background for an aspiring taxonomist. ^[5]

# Data Collection Roles

A taxonomy, regardless of its scope, is only as good as the data that feeds it. ^[9] Developing a job taxonomy involves significant data wrangling, often dealing with inconsistent, legacy, or local naming conventions. Several roles contribute to feeding and validating this raw material.

# Subject Matter Experts

The Subject Matter Expert (SME) is indispensable. These individuals are the operational specialists who actually perform the jobs or manage the people who do. ^[1] If the taxonomy development involves roles like "Full Stack Developer" or specialized functions like a "Site Reliability Engineer," the SME provides the necessary context about the daily tasks, the required competencies, and the seniority level. ^[4] They act as the "ground truth" validator.

For example, an SME might point out that within their department, the title Cloud Architect is used interchangeably with AWS Solution Designer, but that in reality, the Solution Designer role requires deeper networking expertise. Without the SME, the taxonomy might simply group them under a single, generic "Cloud" node, missing a critical nuance needed for accurate workforce intelligence. ^[9]

# HR Analytics and Data Scientists

The HR Analyst or Data Scientist on the team handles the quantitative side of taxonomy creation. They are responsible for mining the existing Human Resources Information System (HRIS) data, looking at historical job title usage, frequency, and associated pay grades or skill assessments. ^[1] They bring the statistical rigor to determine if a proposed classification actually reflects the organization's reality or aspirations.

For example, if the proposed structure separates roles into three distinct groups based on required experience, the Data Scientist must use workforce data to check if the current employee population actually aligns with those three tiers or if the organizational structure itself needs adjustment, which then informs the taxonomy design. ^[6] Their contribution contrasts with the Taxonomist’s conceptual structure by applying an empirical structure based on current facts.

An interesting synthesis emerges when comparing skills vs. job taxonomies here. While a pure job taxonomy might rely more on organizational charts and role definitions, a skills taxonomy development team needs to integrate data from performance reviews, learning management systems, and job requisitions simultaneously to build a weighted model of what skills are truly active versus merely desired. ^[3][10] The Data Scientist’s role becomes significantly more complex in a skills-based build-out.

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# Governance and Oversight

Building the structure is only the first phase; ensuring it remains accurate and governed over time constitutes the second major area of responsibility. Taxonomies are living documents because jobs, markets, and skills constantly shift. ^[10]

# Taxonomy Steward or Owner

The Taxonomy Steward or Product Owner acts as the long-term caretaker of the classification system. Once the development phase concludes, this role takes ownership of change requests, governance policy, and maintenance schedules. ^[5] They are the gatekeepers who decide if a newly proposed job title warrants a new node, a merger with an existing one, or simply a mapping update under an existing category.

This stewardship requires a delicate balance. They must enforce the structural integrity defined by the core architect to prevent "taxonomy drift"—where users start bypassing the established structure because it feels too cumbersome. ^[7] This often means running regular audits to ensure that new hires or job postings are correctly aligned. A common failure scenario in taxonomy adoption is when governance is weak, leading users to revert to free-text searching because the structured taxonomy is perceived as outdated or overly bureaucratic.

# HR Business Partners

The HR Business Partner (HRBP) acts as the primary liaison between the taxonomy development team and the various business units. They translate strategic workforce planning needs into taxonomy requirements and, conversely, translate the technical structure back into actionable insights for their business leaders. ^[1]

Their role in development is primarily advisory and championing. They ensure that the final product solves real business problems—whether that is simplifying internal mobility, standardizing salary banding, or identifying skill gaps across departments. ^[9] A well-integrated HRBP ensures the taxonomy doesn't become a purely academic exercise managed by IT or HR specialists; instead, it remains grounded in operational reality.

# Technology Integration Roles

A job taxonomy must ultimately live within a system, whether it is an Applicant Tracking System (ATS), an HRIS, or a dedicated talent marketplace platform. ^[7] The success of the structure depends on its technical implementation.

# System Integrators and Developers

The System Integrator or Software Developer works closely with the Taxonomist to ensure the technical schema correctly mirrors the conceptual schema. ^[2] This is where the rules of classification meet the limitations or capabilities of the software platform. They write the code or configure the system settings that allow the taxonomy to function, for instance, by building the logic for inheritance or defining how a skill tag propagates up to a job family.

This requires technical skill in the specific platform being used, whether it is a commercial off-the-shelf system or an internally built application. They often translate abstract concepts like "a hierarchical relationship where A is-a-type-of B" into executable database queries or API calls. ^[6] A key insight here is that the technical team often surfaces constraints missed by the conceptual designers; for example, a certain legacy database might be unable to efficiently handle more than five levels of nested hierarchy, forcing the Taxonomist to simplify the structure for practical performance gains.

# Change Management and Communications

While not always a full-time development role, Change Management Specialists are crucial during the rollout phase. Job taxonomy development involves changing how people describe their work—a significant cultural shift in terminology. ^[5] These roles focus on creating training materials, running workshops, and clearly communicating why the old titles are being retired or mapped and how the new structure benefits the end-user (managers filling roles, employees looking for internal moves). ^[1] Without this specific focus on adoption, even a perfectly designed taxonomy can fail due to organizational inertia.

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# Synthesis of Roles and Team Dynamics

The development of a job taxonomy is fundamentally an exercise in cross-domain translation. It is the place where the language of business operations (what people do) meets the language of information science (how to structure that information) and the language of technology (how to store and retrieve that structure). ^[1][7]

Consider a project tackling a wide variety of roles, perhaps looking at all the emerging titles in modern digital operations, such as those seen in data engineering discussions like "ETL Developer," "Data Modeler," or "BI Analyst". ^[4] The Taxonomist might propose grouping these under a parent term like "Data Platform Engineering." The HRBP must confirm this aligns with how the executive team views reporting lines. The SME for Data Engineering must confirm that the skill sets differentiate between an ETL Developer and a Data Modeler sufficiently. The System Integrator must then configure the ATS to enforce this distinction during requisition creation.

An actionable tip for establishing a successful taxonomy development team involves explicitly defining the "Three Layers of Ownership" from the outset. The Conceptual Owner (Taxonomist) owns the rules. The Data Owner (HR Analyst/SME group) owns the validity of the input. The Technical Owner (Developer) owns the performance and deployment mechanism. Conflicts often arise when one role tries to dominate all three layers, which typically results in a structure that is either too abstract or too technically constrained.

Furthermore, when assessing the required depth, especially in rapidly evolving fields, an important distinction emerges: organizations must decide if they are building a classification taxonomy (focused on categorizing existing jobs) or a discovery taxonomy (focused on enabling future workforce movement and gap analysis). ^[9] A discovery taxonomy, which relies heavily on granular skills mapping, inherently requires more continuous input from SMEs and higher computational validation from Data Scientists than a simpler classification structure. ^[3][10] This decision directly influences the required size and seniority of the roles assigned to the project. The output must serve the ultimate goal of workforce intelligence, meaning its structure must facilitate forward-looking questions, not just retrospective reporting. ^[9] The roles involved must therefore be skilled not just in cataloging, but in anticipating future business needs based on current market signals. ^[6]