Torchora
Back to Home

Key Responsibilities and Required Skills for a Knowledge Engineer

💰 Competitive Salary - DOE (Depending on Experience)

Data ScienceAI/MLSoftware EngineeringData Engineering

🎯 Role Definition

As a Knowledge Engineer, you are the architect of understanding at our company. You will be at the critical intersection of data science, software engineering, and business intelligence, responsible for transforming disparate data and domain expertise into a structured, machine-readable knowledge base. Your primary mission is to design, build, and maintain our enterprise knowledge graph, creating the semantic layer that powers advanced analytics, intelligent search, and cutting-edge AI/ML applications. This role is for a curious and meticulous problem-solver who is passionate about structuring information and enabling machines to reason and comprehend complex domains in a human-like way.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Scientist / ML Engineer
  • Software Engineer (with a data-intensive or backend focus)
  • Data Analyst / Business Intelligence Analyst
  • Ontologist / Taxonomist / Information Scientist

Advancement To:

  • Senior / Principal Knowledge Engineer
  • Knowledge Graph Architect
  • AI / ML Product Manager
  • Director of Data Science or Data Engineering

Lateral Moves:

  • Data Architect
  • Senior Data Scientist
  • Machine Learning Engineer

Core Responsibilities

Primary Functions

  • Architect, construct, and evolve sophisticated ontologies, taxonomies, and knowledge graphs that serve as the central nervous system for our intelligent applications.
  • Translate complex business requirements and abstract domain knowledge into formal, robust semantic models using standards like RDF, RDFS, and OWL.
  • Develop and optimize complex SPARQL queries to perform deep data interrogation, validation, and insight extraction from our graph data stores.
  • Collaborate intimately with data scientists and ML engineers to embed the knowledge graph into advanced machine learning pipelines, enhancing model features and explainability.
  • Design, implement, and manage resilient data ingestion pipelines to continuously populate and enrich the knowledge graph from diverse structured and unstructured sources.
  • Lead collaborative knowledge acquisition sessions with subject matter experts (SMEs) across the business to elicit, capture, and formally model their specialized domain expertise.
  • Establish and champion data modeling standards and governance best practices for knowledge representation to ensure consistency and quality across the organization.
  • Evaluate, benchmark, and recommend cutting-edge graph database technologies (e.g., Neo4j, Stardog, Amazon Neptune) and semantic tooling to keep our stack modern and effective.
  • Engineer and integrate Natural Language Processing (NLP) components for named entity recognition (NER), relationship extraction, and text classification to automatically harvest knowledge from text.
  • Develop and maintain exceptionally clear and comprehensive documentation for our ontologies, data models, and knowledge engineering workflows to support wider team adoption.
  • Design and implement sophisticated reasoning and inference rules using technologies like SHACL or OWL to automatically validate data quality and derive new, implicit knowledge.
  • Build and expose robust APIs and microservices that provide clean, programmatic access to the knowledge graph for a wide range of downstream applications and analytical users.
  • Conduct rigorous quality assurance, consistency checks, and validation procedures to maintain the integrity, accuracy, and completeness of the knowledge graph.
  • Act as a thought leader by staying ahead of the curve on the latest advancements in semantic web technologies, graph-based AI, and large language model (LLM) integration.
  • Own the complete lifecycle management of our semantic assets, including establishing robust versioning, change control, and deployment processes for ontologies and models.
  • Develop custom scripts and automation tools, primarily in Python, to streamline knowledge extraction, transformation, and loading (ETL) processes for the graph.
  • Drive the strategic adoption of semantic technologies and knowledge-centric design patterns to solve the company's most challenging data integration, search, and analytics problems.
  • Partner closely with product managers and UX/UI designers to create intuitive and powerful data exploration tools and user-facing applications that leverage the knowledge graph.
  • Lead research initiatives and build proof-of-concepts (PoCs) to explore novel applications of knowledge graphs for product innovation and strategic business intelligence.
  • Mentor junior engineers and data analysts, fostering a culture of excellence and deep expertise in knowledge engineering principles and semantic best practices.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis using graph query languages.
  • Contribute to the organization's overarching data strategy and technology roadmap.
  • Collaborate with business units to translate ambiguous data needs into concrete engineering requirements.
  • Participate in sprint planning, retrospectives, and other agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

  • Semantic Web Technologies: Deep proficiency in standards like RDF, RDFS, OWL, and SHACL for data modeling and constraints.
  • Graph Query Languages: Expertise in writing and optimizing complex SPARQL queries. Cypher is a plus.
  • Programming: Strong programming skills, particularly in Python, for data processing, API development, and automation (e.g., using libraries like RDFLib, Pandas, FastAPI).
  • Graph Databases: Hands-on experience with at least one major graph database, such as Stardog, Neo4j, Amazon Neptune, or similar triple stores.
  • Data Modeling: Exceptional ability to model complex domains, create taxonomies, and design robust ontologies from the ground up.
  • NLP Fundamentals: Experience with Natural Language Processing techniques for information extraction, including entity recognition (NER) and relationship extraction.
  • ETL/Data Pipelines: Proven ability to design and build data pipelines to ingest and transform data from various sources (APIs, databases, flat files).
  • Cloud Environments: Familiarity with deploying and managing data solutions in a major cloud platform (AWS, GCP, or Azure).

Soft Skills

  • Analytical & Problem-Solving: A powerful ability to deconstruct complex problems, think algorithmically, and design elegant, scalable solutions.
  • Communication & Collaboration: Superb verbal and written communication skills, with a special talent for bridging the gap between technical engineers and non-technical domain experts.
  • Intellectual Curiosity: A genuine passion for learning new domains, technologies, and methodologies, coupled with a desire to understand the "why" behind the data.
  • Meticulous Attention to Detail: An uncompromising focus on data quality, consistency, and accuracy in modeling and implementation.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's Degree in a quantitative or technical field.

Preferred Education:

  • Master's or PhD in Computer Science, Information Science, or a related discipline with a published focus on AI, Semantics, Knowledge Representation, or NLP.

Relevant Fields of Study:

  • Computer Science
  • Information Science / Library Science
  • Linguistics
  • Data Science

Experience Requirements

Typical Experience Range: 3-7+ years in a related role such as Data Engineering, Software Engineering, or Data Science.

Preferred: Demonstrable experience building, deploying, and maintaining a large-scale knowledge graph in a production environment is highly desirable. A portfolio of relevant projects or a public GitHub profile is a strong plus.

Explore More Careers