Semantic Data Modeler
About The Position
The Semantic Data Modeler develops and maintains the enterprise data platform’s semantic layer so business data is consistent, discoverable, and reusable across enterprise analytics and AI applications. Working with data architecture, engineering, governance, and business teams, the role translates business concepts into standardized data models, relationships, and metadata that support business requirements, generative AI, retrieval-augmented generation (RAG), natural language querying, and AI-powered applications. The role designs and maintains conceptual, logical, physical, dimensional, and semantic models; maps business concepts to data assets; and enhances metadata, semantic consistency, and contextual relevance. It incorporates data quality, lineage, privacy, security, and access controls into modeling practices and establishes reusable patterns, naming conventions, and design standards. The modeler facilitates workshops and design reviews, documents decisions, and develops an understanding of AWS-based data pipelines and flows to keep models aligned with source systems, transformations, analytics, and AI applications.
Requirements
- Bachelor’s degree in Computer Science, Information Systems, Data Management, or a related discipline, or equivalent practical experience.
- 7+ years of experience in data modeling, semantic modeling, data architecture, or enterprise data management.
- Demonstrated experience designing and maintaining conceptual, logical, physical, dimensional, or semantic data models and translating business requirements into reusable models and standardized definitions.
- Strong SQL skills and understanding of relational databases, data structures, entity relationships, and data integration concepts.
- Experience with metadata management, data governance, data quality, and data lineage practices.
- Understanding of AWS-based data pipelines, including data ingestion, transformation, orchestration, and data flows across enterprise data platforms.
- Willingness to travel as required.
Responsibilities
- Design, develop, and maintain conceptual, logical, physical, dimensional, and semantic data models aligned with enterprise architecture standards and business requirements.
- Translate business concepts and requirements into reusable models defining entities, attributes, relationships, business rules, KPIs, and standardized definitions.
- Map business concepts to underlying data assets and maintain semantic metadata for analytics, generative AI, RAG, natural language querying, and AI agents.
- Collaborate with Enterprise Architecture, data engineering, governance, analytics, AI teams, and business stakeholders to validate definitions and align models with enterprise standards.
- Improve semantic consistency, metadata completeness, and contextual relevance to enhance data quality and AI application outcomes.
- Incorporate data quality, lineage, privacy, security, and access controls into modeling practices to support trusted and responsible data use.
- Establish reusable modeling patterns, naming conventions, and design standards to reduce duplicated business logic and conflicting definitions across data domains.
- Facilitate modeling workshops and design reviews, document key decisions, and promote consistent semantic modeling practices across the organization.
- Understand AWS-based data pipelines and data flows, including ingestion, transformation, and delivery, to ensure semantic models align with source systems and downstream applications.
- Prepare and enhance semantic models and metadata for generative AI, RAG, natural language querying, AI agents, and enterprise AI applications.
- Travel as required.
Benefits
- Annual bonus potential of 10%
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What This Job Offers
Job Type
Full-time
Career Level
Senior
