The job, in the employer's words
Tiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.
We are looking for an Ontology Architect to lead high-impact applied Engineering and analytics initiatives. This role combines deep technical expertise, strong experimentation rigor, and business leadership to influence product direction and drive measurable outcomes at scale.
Responsibilities:
- Ontology & Taxonomy Design: Lead the creation, modeling, and evolution of enterprise-wide ontologies, taxonomies, and controlled vocabularies that accurately represent complex business domains.
- Knowledge Graph Architecture: Design and implement scalable architecture, ingestion pipelines, and governance for enterprise Knowledge Graphs (Triple Stores or Property Graphs).
- Semantic Layer Strategy: Build and maintain the enterprise semantic layer to abstract physical data complexities, providing a unified, machine-readable business view of data.
- Data Product Augmentation: Partner with domain data teams to map, link, and augment decentralized Data Products using the central ontology to ensure semantic interoperability across the organization.
- Inference & Reasoning: Implement semantic reasoning and inference rules to automatically generate new metadata and uncover hidden insights within the graph.
- Governance & Standards: Establish best practices, version control mechanisms, and data contracts for semantic models, ensuring consistent graph schema updates across business units.
Requirements
- Semantic Standards: Expert-level mastery of core semantic technologies
- Knowledge Graph Engineering: Hands-on experience designing and operating production-grade Graph Databases / Triple Stores (e.g., GraphDB, Stardog, Amazon Neptune, AllegroGraph, or Neo4j).
- Ontology Modeling Tools: Proficiency with industry-standard ontology engineering and taxonomy management software (e.g., Protégé, TopBraid Composer, PoolParty).
- Modern Data Frameworks: Clear, practical understanding of Data Mesh paradigms, specifically how to design a semantic layer that overlays federated, domain-driven Data Products.
- Traditional Data Modeling: Strong baseline in classic data concepts, including relational databases, dimensional modeling, and ETL/ELT integration patterns.
Preferred Skills
- Data Quality & Validation: Hands-on experience to enforce data quality and constraint validation across graph structures.
- Advanced AI & Graph Analytics: Familiarity with graph algorithms, graph machine learning (GNNs), or leveraging Knowledge Graphs to enhance Large Language Model architectures via Graph RAG (Retrieval-Aug Generation).
Benefits
Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.
Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.
Benefits mentioned
- Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.
- Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.
Requirements mentioned
- Semantic Standards: Expert-level mastery of core semantic technologies
- Knowledge Graph Engineering: Hands-on experience designing and operating production-grade Graph Databases / Triple Stores (e.g., GraphDB, Stardog, Amazon Neptune, AllegroGraph, or Neo4j).
- Ontology Modeling Tools: Proficiency with industry-standard ontology engineering and taxonomy management software (e.g., Protégé, TopBraid Composer, PoolParty).
- Modern Data Frameworks: Clear, practical understanding of Data Mesh paradigms, specifically how to design a semantic layer that overlays federated, domain-driven Data Products.
- Traditional Data Modeling: Strong baseline in classic data concepts, including relational databases, dimensional modeling, and ETL/ELT integration patterns.
- Data Quality & Validation: Hands-on experience to enforce data quality and constraint validation across graph structures.
- Advanced AI & Graph Analytics: Familiarity with graph algorithms, graph machine learning (GNNs), or leveraging Knowledge Graphs to enhance Large Language Model architectures via Graph RAG (Retrieval-Aug Generation).
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