Key Responsibilities and Required Skills for Knowledge Architect Assistant

🎯 Role Definition

The Knowledge Architect Assistant supports the creation and operationalization of structured knowledge assets — taxonomies, ontologies, knowledge graphs, metadata models, and semantic layers — to improve findability, interoperability, and machine understanding across products and workflows. Working closely with Knowledge Architects, Data Engineers, NLP teams, and business stakeholders, you will help translate business requirements into semantic models and implement best practices for governance, ingestion, and lifecycle management.

This is a hands-on role focused on semantic modeling, data integration, metadata strategy, and tooling for enterprise knowledge systems, with strong emphasis on applied standards (RDF/OWL/SKOS/JSON-LD), graph databases, and search/RAG integrations.

📈 Career Progression

Typical Career Path

Entry Point From:

Knowledge Analyst / Taxonomy Specialist
Data Analyst with metadata responsibilities
Librarian / Information Specialist with controlled vocabularies experience
Ontology Engineer or Semantic Engineer (junior)
Content Strategist focused on metadata and tagging

Advancement To:

Knowledge Architect / Senior Knowledge Architect
Lead Ontology Engineer or Head of Knowledge Engineering
Director of Knowledge & Content Strategy
Head of Data, Knowledge Graphs or Chief Data Officer (CDO) for larger organizations

Lateral Moves:

Product Manager (Search/Discovery)
Data Governance Lead / Master Data Management (MDM) Manager
Information Architecture or UX Research Lead

Core Responsibilities

Primary Functions

Design, document and maintain enterprise ontologies and knowledge graphs that model core business domains, using standards such as RDF, OWL and SKOS to ensure semantic rigor and interoperability across systems.
Develop and operationalize taxonomies, controlled vocabularies, metadata schemas and tagging strategies to improve content discoverability, faceted search, and consistent classification across multiple content repositories.
Translate business requirements into semantic models: perform entity and relationship discovery, canonicalization, and normalization to create a reusable knowledge layer that supports search, analytics and AI applications.
Collaborate with NLP and ML engineers to define annotation schemas, entity recognition, relation extraction pipelines, and training data needs; help design labeling workflows and quality assurance processes for model training.
Build and maintain ETL/ingestion pipelines to populate and refresh knowledge graphs and metadata stores from diverse structured and unstructured sources (databases, CMS, data lakes, APIs), ensuring lineage and provenance tracking.
Implement and tune graph database solutions (Neo4j, Amazon Neptune, JanusGraph, etc.) and query endpoints using SPARQL, Cypher or Gremlin to enable efficient retrieval, analytics and integrations.
Integrate knowledge layers with search engines and vector search solutions (Elasticsearch, OpenSearch, Milvus, Pinecone) to support semantic search, retrieval-augmented generation (RAG), chatbots and recommendation systems.
Establish and enforce knowledge governance practices including stewardship roles, access controls, versioning, change management, and lifecycle policies for ontologies and taxonomies.
Define and track KPIs and success metrics for knowledge initiatives (search relevance, entity coverage, retrieval precision/recall, time-to-insight) and drive continuous improvement based on analytics and user feedback.
Evaluate, recommend and onboard knowledge tooling — taxonomy management platforms, ontology editors (Protégé), graph DBs, metadata catalogs and annotation tools — aligning tool choices to scale and interoperability requirements.
Produce clear, practical documentation and developer-friendly API contracts for knowledge graph endpoints, metadata models and schema definitions to accelerate adoption across product and engineering teams.
Coordinate cross-functional workshops and stakeholder interviews to surface domain knowledge, prioritize taxonomy/ontology workstreams, and align knowledge architecture roadmaps with business goals.
Perform regular content and metadata audits, identify gaps and inconsistencies, and execute remediation plans (re-tagging, normalization, mapping legacy taxonomies) to improve data quality and discoverability.
Create and maintain mapping strategies for integrating external ontologies, industry taxonomies and schema.org/JSON-LD to maximize reuse and external interoperability.
Support the design and implementation of entity resolution and master entity services to reduce duplication and ensure canonical representations across systems.
Prototype and implement semantic search features, autocomplete, synonyms, and faceted navigation to improve user experience and search relevance for internal tools and customer-facing products.
Collaborate with product managers and UX designers to embed knowledge-driven features—contextual suggestions, knowledge cards, and conversational assistants—into end-user workflows.
Maintain governance around semantic annotations for documents and content, defining annotation guidelines and supervising quality control for manual and automated tagging.
Drive adoption by delivering training, playbooks and onboarding sessions for business units and data stewards on taxonomy usage, metadata tagging and knowledge tools.
Support incident response and triage for knowledge services, including debugging graph queries, resolving mapping errors and coordinating fixes with engineering teams.
Research and pilot emerging approaches such as vector embeddings, hybrid semantic+lexical retrieval, prompt engineering for LLMs, and multimodal knowledge linking to extend the capabilities of the knowledge platform.
Manage incremental releases and sprint-level tasks for knowledge architecture initiatives, participating in agile ceremonies and maintaining clear backlog priorities aligned to product roadmaps.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

Ontology and taxonomy design: RDF, OWL, SKOS, schema.org and controlled vocabulary development.
Knowledge graph modeling and implementation experience with graph databases (Neo4j, Amazon Neptune, JanusGraph).
Semantic query languages and APIs: SPARQL, Cypher, Gremlin and familiarity with REST/GraphQL endpoints for knowledge services.
Metadata modeling and data cataloging experience, including metadata standards and governance.
Familiarity with NLP techniques and tools: NER, relation extraction, spaCy, transformers, and annotation workflows.
Experience with vector embeddings, semantic similarity, vector databases (Milvus, Pinecone, FAISS) and implementing RAG/semantic search.
Strong scripting and data transformation skills in Python (pandas), SQL, and knowledge of ETL/workflow tools (Airflow, NiFi, dbt).
Practical experience with JSON-LD, linked data principles and interoperability patterns across heterogeneous systems.
Hands-on exposure to search platforms (Elasticsearch, OpenSearch) and tuning relevance, synonyms and ranking models.
Understanding of data governance, lineage, access controls, and stewardship practices for enterprise knowledge assets.
Familiarity with ontology editors and tools such as Protégé, TopBraid, PoolParty, or similar taxonomy/ontology management platforms.
Basic cloud competencies: AWS/GCP/Azure services commonly used for graph and ML deployments.
Experience working with APIs, microservices and integrating knowledge layers into product stacks.
Version control, CI/CD practices, and experience collaborating with engineering teams on production deployments.

Soft Skills

Excellent verbal and written communication; able to translate technical models to business stakeholders and vice versa.
Strong stakeholder management and facilitation skills for cross-functional alignment and requirements elicitation.
Analytical mindset with attention to detail for schema design, data quality and model validation.
Problem-solving orientation with the ability to prototype and iterate quickly.
Collaborative team player who can work across data, engineering, product and business teams.
Project management and prioritization skills; experience operating within Agile frameworks.
Ability to teach and evangelize taxonomy/metadata best practices across distributed teams.
Curiosity and continuous learning mindset to keep up with semantic technologies and LLM/AI trends.
Empathy for end users and focus on improving discoverability, relevance and user workflows.
Diplomacy and change-management skills to shepherd taxonomy/ontology changes through stakeholders.

Education & Experience

Educational Background

Minimum Education:

Bachelor’s degree in Computer Science, Information Science, Library & Information Studies, Knowledge Management, Linguistics, or a related field.

Preferred Education:

Master’s degree in Information Science, Knowledge Management, Computational Linguistics, Artificial Intelligence, or equivalent experience.

Relevant Fields of Study:

Information Science / Library Science
Computer Science / Data Science
Linguistics / Computational Linguistics
Knowledge Management / Business Informatics
Semantic Web / Artificial Intelligence

Experience Requirements

Typical Experience Range:

3–7 years of progressive experience in knowledge management, ontology/taxonomy design, data architecture, or knowledge engineering roles.

Preferred:

5+ years building or supporting enterprise knowledge graphs, ontologies, taxonomy programs or semantic search solutions; demonstrated experience integrating knowledge layers with ML/NLP and search systems.