7+ years in data modeling and enterprise data architecture
Strong experience in designing data models for enterprise platforms
Proficient in metadata, master/reference data, and data lineage
Hands-on experience with Databricks, Delta Lake, and Unity Catalog
Ability to convert unstructured content into structured data models
Responsibilities
Design logical and physical data models for unstructured content
Define ownership boundaries for data products
Establish metadata standards and tagging taxonomies
Assign and enforce data security classifications
Register and maintain data products in Databricks Unity Catalog
Collaborate with stakeholders to align data product design with consumption needs
Document repeatable modeling standards for future onboarding
Benefits
100% remote working opportunity across the USA/Canada
Full-time employment status
Opportunity to contribute to a foundational KM Data Platform team
Engagement in a multi-stakeholder environment that impacts knowledge management strategy
Professional growth in emerging fields like knowledge modeling and data governance
Full Job Description
Job Description
Role - Senior Data Modeler Working Model - 100% Remote (USA/Canada) Employment type - Fulltime Numbers of Interviews - 2 or 3
Role Summary:
Senior Data Modeler Unstructured Data / Knowledge Management
The Senior Data Modeler will design and govern the data architecture for unstructured knowledge assets across Client's Knowledge Management (KM) ecosystem.
This role bridges data engineering discipline with KM domain expertise, translating raw unstructured content (documents, case files, informal knowledge captures, chat/email extracts, etc.) into well-defined, discoverable, and secure data products within Databricks Unity Catalog.
This is a foundational hire for a newly forming KM Data Platform team supporting Client's broader Knowledge and Research Systems strategy.
Mandatory Skills:
7+ years of experience in data modeling, information architecture or enterprise data architecture.
Strong experience designing conceptual, logical and physical data models for enterprise data platforms.
Strong understanding of entities, relationships, metadata, master/reference data and data lineage.
Experience with taxonomy, ontology, semantic models and controlled vocabularies.
Hands-on experience with Databricks, Delta Lake and Unity Catalog or comparable modern data platforms.
Ability to translate business concepts and unstructured information into structured, reusable data models.
Good to Have Skills:
Experience designing knowledge graphs, ontologies and semantic knowledge models.
Experience with GenAI/RAG knowledge models and vector/embedding representations.
Experience modeling documents, document elements, entities, relationships, evidence and provenance.
Knowledge of knowledge graph technologies such as Neo4j, RDF or property graphs.
Experience with Commercial/Customer/CRM domain models, SharePoint content or enterprise knowledge platforms.
Key Responsibilities:
Design logical and physical data models for unstructured and semi-structured content (documents, case artifacts, K-Slices, extracted knowledge fragments, metadata records) originating from KM pipelines such as case mining and informal knowledge capture workflows.
Define domain boundaries and ownership for data products - determining what constitutes a discrete, reusable data product versus a raw or intermediate asset.
Establish metadata standards and tagging taxonomies (content type, practice/domain, provenance, confidentiality, freshness, lineage) to ensure consistent classification across knowledge sources.
Assign and enforce security and sensitivity classifications on data products in line with firm data governance, privacy, and legal/risk requirements.
Register, document, and maintain data products in Databricks Unity Catalog, including schemas, access grants, lineage, and catalog-level metadata.
Partner with data engineers building Databricks pipelines to ensure ingestion, transformation, and storage patterns align to the modeled domain structure.
Collaborate with Knowledge Products, Research Products, and Architecture/Data/Technology stakeholders to align data product design with downstream consumption needs (e.g., surfacing in Sage/Glean, AI agent retrieval).
Support privacy and legal review processes by ensuring data products are classified and documented to enable timely sign-off.
Establish and document repeatable modeling standards/playbooks so future data products can be onboarded consistently as the KM platform scales.
Required Qualifications:
5+ years of experience in data modeling, data architecture, or information architecture, with meaningful exposure to unstructured or semi-structured data (not purely relational/transactional modeling).
Direct experience working in or adjacent to Knowledge Management, content management, or enterprise search domain - understands how documents, case files, or knowledge artifacts differ from standard transactional data.
Hands-on experience with a modern data catalog; Databricks Unity Catalog experience strongly preferred.
Demonstrated ability to define data domains and data product boundaries in a large, multi-stakeholder organization.
Practical knowledge of metadata management: tagging schemas, taxonomies, controlled vocabularies, or ontology design.
Understanding of data security/sensitivity classification frameworks and how they map to access control in a lakehouse environment.
Experience partnering with data engineering teams on ingestion and pipeline design (not required to write production pipeline code, but must speak the language).
Strong written and verbal communication skills; able to translate technical modeling decisions into business-readable rationale for KM stakeholders and governance reviewers.
Preferred Qualifications:
Experience with enterprise knowledge platforms (e.g., Glean, SharePoint, ServiceNow) or AI-powered retrieval systems.
Familiarity with Databricks Delta Lake, Delta Sharing, or Lakehouse Federation.
Prior experience in professional services, consulting, or a similar document/case-intensive knowledge environment.
Exposure to Legal/Risk/Privacy review processes for data classification and access approvals.
Background in library science, information science, or applied ontology is a plus but not required. Success. Metrics (First 6-12 Months)
Domain model and metadata taxonomy defined and adopted for at least one major KM data product line (e.g., case mining outputs, informal knowledge K-Slices).
Data products registered and discoverable in Unity Catalog with correct security classifications applied.
Documented, repeatable modeling standard that engineering and future modelers can apply without re-litigating domain boundaries each time.
Reduced turnaround time on privacy/legal classification reviews due to upfront, consistent metadata and tagging.
Qualifications
Must have skills:
Minimum 5 years of experience.
Data Modeling (Strong), Databricks, Taxonomy Ontology Semantic models and Delta Lake and Unity Catalog, enterprise data architecture.
