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Stop defining roles from scratch: build better role profiles
Use structured occupational data to find relevant responsibilities, skills, knowledge, and activities, then adapt them to build roles that fit your organization.
MarkusYou want to define a Product Manager role. The title is familiar, and you know roughly what the person should achieve. Then the empty role form raises harder questions.
Which responsibilities belong in the role? Which skills and knowledge matter? What is fundamental to the occupation, and what only makes sense in your company? What have you overlooked?
Most organizations piece together an answer from existing job descriptions, job advertisements, web searches, AI-generated lists, and internal opinions. Each can help. But none gives the team a stable, shared starting point.
There is another source: structured occupational data. It will not define the role for you, but it can make the first draft much better.
The blank page is not neutral
Starting from nothing sounds flexible. In practice, it makes the quality of a role profile depend heavily on who writes it, which examples they find, and what they remember at that moment.
One manager may describe outcomes. Another may list daily tasks. A third may copy requirements from a vacancy. The resulting role profiles are difficult to compare, even when the roles are closely related.
Structured occupational databases provide a reference vocabulary. Depending on the source, they organize information such as occupation titles, descriptions, skills, knowledge, activities, tasks, qualifications, and relationships to other occupations.
The practical idea is simple: occupational data provides the baseline; your organization provides the context.
A surprising amount of occupational knowledge already exists
Occupational databases are maintained reference systems, often created by public institutions for employment services, statistics, education, career guidance, or labor-market research. They turn information that would otherwise sit in documents and individual experience into structured concepts and relationships.
The sources are not interchangeable. A statistical classification may give occupations consistent codes but little detail about the work. A richer occupational profile database may connect an occupation with skills, knowledge, tasks, or work activities. National systems can add local titles, training routes, and certificates.
For role design, the second kind is especially useful. It gives you material to evaluate instead of asking you to recall every relevant element unaided.
ESCO, AMS BIS, O*NET, and other useful sources
You do not need to become a taxonomy specialist to use occupational data. A few examples show how different sources can contribute.
ESCO: a shared European language
ESCO is the European Commission's multilingual classification of occupations and skills. It works like a structured dictionary: it describes occupational and skill concepts and shows relationships between them. Its skills pillar includes knowledge, skills, and competences.
That makes ESCO useful when organizations need consistent terminology across roles, systems, countries, or languages. Its strength is the shared vocabulary—not a detailed description of how a particular company should organize Product Management.
AMS BIS: practical depth for Austria
The AMS Berufsinformationssystem (BIS) connects Austrian occupational profiles with activities, competencies, training, and certificates. Its competency classification structures professional and cross-functional competencies and supports matching between people and occupational fields, as described in the official AMS system guide.
This local depth matters. Common job titles, vocational pathways, and expectations do not map perfectly from one labor market to another.
O*NET: detailed information about work in the United States
O*NET, sponsored by the U.S. Department of Labor, provides detailed occupation profiles for the U.S. economy. Its content model covers skills, knowledge, abilities, work activities, tasks, work context, education, and experience.
That depth is valuable for research and role exploration. It should still be interpreted in context: information collected for U.S. occupations is not automatically the right definition for a European organization.
Different labor markets have their own reference systems
Other systems serve similar purposes in their own labor markets. Examples include ROME 4.0 in France and BERUFENET in Germany. Some sources are rich occupational profiles; others primarily classify occupations or observe demand in job advertisements.
The useful question is not “Which database is best?” It is “Which source gives us a credible reference for this role, in this labor market, for this purpose?”
Occupational data sources at a glance
The following public reference systems and commercial additions solve different problems, so inclusion here does not mean that every source is equally suitable for role design or that riteful integrates every source.
Europe
- ESCO: European multilingual reference for occupations and skills.
- AMS BIS: Austrian occupation profiles, competencies, training, and certificates.
- BERUFENET, KldB, and DKZ: German profiles, classification, and machine-readable catalogs.
- ROME 4.0: French occupations, skills, activities, and work contexts.
- Cedefop Skills Intelligence and Skills-OVATE: European labor-market and skill trends from multiple data sources.
- Atlante del Lavoro: Italian activities, competencies, and qualification relationships.
- CNO-11, CO-SISPE, and SEPE profiles: Spanish classification, employment-service occupations, and profiles.
North and Latin America
- O*NET: Detailed U.S. profiles covering skills, knowledge, tasks, and context.
- SOC: U.S. statistical standard for occupation codes, titles, and hierarchies.
- NOC and OaSIS: Canadian occupational classification and complementary skill profiles.
- SINCO: Mexico's national occupational classification.
- CBO, QBQ, and GBO: Brazilian occupations with knowledge, skills, attitudes, and market data.
- CUOC and OCUPACOL: Colombian occupation codes and semantically rich profiles.
Commercial additions
- Lightcast: Skill, job-title, and labor-market taxonomies for matching and analysis.
- Textkernel: Multilingual skill and occupation taxonomies with NLP normalization.
From “Product Manager” to a useful role profile
Suppose Product Manager is your starting label. Depending on the source and occupational match, reference data may surface candidate material such as:
- Activities and responsibilities: analyzing market or customer needs, defining products, coordinating development and launch, and following the product lifecycle.
- Skills: communication, project management, management, and marketing.
- Knowledge: business fundamentals, sales, the relevant market, and industry-specific subject matter.
These are candidates, not a finished specification. Even the title is ambiguous. A Product Manager for industrial equipment, a retail assortment, and a software platform may share a few patterns while doing substantially different work. The AMS description of Product Manager illustrates this clearly: its emphasis on production, sales, market analysis, and market launch is useful reference material, but it will not fully describe every digital product role.
The organization now has something concrete to discuss:
- Does this role own product strategy or contribute to it?
- Which decisions can it make without approval?
- Is discovery, delivery, go-to-market, or the whole lifecycle in scope?
- What product, market, regulatory, or technical knowledge is essential here?
- How does the role change across seniority levels?
This is where a generic occupation begins to become your role.
Reference data is not your organizational truth
An occupation and an organizational role are related, but they are not the same thing. The ESCO handbook distinguishes an occupation—a group of jobs with similar main tasks and duties—from a job carried out for a particular employer. A role profile adds another layer: the expectations, accountabilities, and relationships that apply inside your organization.
No external database knows your:
- strategy and operating model
- products, customers, and regulatory environment
- decision rights and reporting relationships
- career and seniority framework
- division of work between neighboring roles
- outcomes for which this particular role is accountable
Treating reference data as a template to copy would only replace one weak shortcut with another. Its value is that it makes relevant possibilities visible. Your team still decides what belongs, what does not, and how each element should be expressed.
How riteful turns occupational data into a starting point for role design
riteful combines AI assistance with structured occupational and competency data from sources such as ESCO and AMS BIS, giving HR a stronger starting point for defining a role.
The reference data suggests what may be relevant. Your organization decides what actually belongs in the role.
Instead of facing an empty form, HR can start with relevant structured information, review it, and adapt it. The organization remains responsible for the final role: people can remove irrelevant suggestions, change the language, add company-specific expectations, and clarify accountability.
This approach can improve role design in three practical ways:
- Less blank-page work. The first useful questions and candidate concepts are already available.
- More consistent language. Related roles can draw on a common reference vocabulary instead of unrelated source documents.
- Better review conversations. Stakeholders can react to a concrete baseline and focus their time on the differences that matter.
riteful's assistance supports human judgment. It does not autonomously decide what a role should be, and the reference sources do not replace organizational ownership.
Stop starting from a blank page
A good role profile is neither a generic occupation copied from a database nor a collection of internal opinions written from memory. It combines a credible external baseline with deliberate organizational choices.
Occupational data can tell you what is commonly associated with an occupation. Your organization determines what this role is here. riteful helps turn the first layer into a practical starting point for defining the second.