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Senior Ontologist (Data modeling, Snowflake/AWS)

McKinsol Consulting IncCharlotte, NC🇺🇸United States

Quick overview

SeniorityMid Senior
Work modeOn Site
LocationCharlotte, NC, United States

Job description

Location: Atlanta(GA), Charlotte (NC), Raleigh (NC)

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, SageMaker, 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, SageMaker, 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.

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.

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