Senior Ontologist; Lending Domain​/Snowflake​/AWS

Senior Ontologist (Lending Domain / Snowflake / AWS)

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.

We are currently seeking a Senior Ontologist (Lending Domain / Snowflake / AWS) to join our team in Charlotte, North Carolina (US-NC), United States (US).

Day-to-Day

Job Duties:

(What this person will do on a daily/weekly basis)

• Design, develop, and maintain business ontologies and semantic models for US lending products, processes, and enterprise data assets.
• Define business concepts, entities, relationships, taxonomies, and metadata standards across commercial, wholesale, consumer, mortgage, and credit lending.
• Model the end-to-end lending lifecycle, including loan origination, underwriting, servicing, credit risk, and collections.
• Collaborate with business stakeholders, data architects, data engineers, governance teams, and AI/ML teams to align semantic models with enterprise data products.
• Map ontology concepts and relationships to physical data models, Snowflake data assets, and enterprise data catalogs.
• Develop and maintain knowledge graphs and semantic layers to support data discovery, lineage, integration, and AI-enabled use cases.
• Leverage AWS services, including Amazon Neptune, Sage Maker, and IAM, to support graph modeling, AI/ML initiatives, and secure data access.
• Apply semantic technologies and standards such as RDF, OWL, and SPARQL to develop and manage ontology models.
• Establish ontology governance, naming conventions, metadata standards, and reusable modeling practices.
• Support data governance, data quality, metadata management, and enterprise data management initiatives.
• Partner with AI/ML teams to enable AI-ready data assets, semantic search, knowledge graph applications, and data-driven solutions.
• Evaluate and improve ontology models based on evolving business requirements, data architecture, regulatory needs, and enterprise standards.

Basic Qualifications:

(What are the skills required for this job with minimum years of experience on each)

• Minimum 7+ years of experience in data modeling, semantic modeling, ontology engineering, knowledge management, or enterprise data architecture.
• Minimum 5+ years of experience in ontology modeling, semantic technologies, knowledge graphs, or taxonomy management.
• Minimum 5+ years of experience in Banking, Financial Services, or lending-related data and business processes.
• Minimum 3+ years of experience with US lending products, including loan origination, underwriting, servicing, credit risk, or collections.
• Minimum 3+ years of hands-on experience with Snowflake and enterprise data environments.
• Minimum 2+ years of experience working with AWS services, preferably Amazon Neptune, Sage Maker, and IAM.
• Strong understanding of RDF, OWL, SPARQL, graph databases, metadata management, and semantic data modeling.
• Experience mapping business concepts and ontologies to physical data models, data catalogs, and enterprise data products.
• Experience supporting AI/ML, data governance, data quality, and enterprise data management initiatives.
• Strong analytical, problem-solving, documentation, and stakeholder collaboration skills.
• Ability to translate complex lending concepts into reusable semantic models and business-friendly terminology.

Travel:
As required by business needs.

Degree:
Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, or a related field, or equivalent professional experience.

Nice to Have:

• Experience with FIBO (Financial Industry Business Ontology) or BIAN (Banking Industry Architecture Network).
• Experience building AI-ready data products, semantic layers, and knowledge graph solutions for financial services.
• Hands-on experience with Amazon Neptune or other graph database platforms.
• Experience with Alation, Collibra, or similar enterprise data catalog and metadata management tools.
• Knowledge of regulatory reporting, data lineage, data governance, and financial services data standards.
• Experience collaborating with data engineering, enterprise architecture, and AI/ML teams on large-scale data initiatives.