5+ years in data modeling or data architecture, particularly with unstructured data.
Strong experience with Databricks and data catalogs, ideally Unity Catalog.
Familiarity with Knowledge Management or content management systems.
Expertise in defining data product boundaries in complex organizations.
Practical knowledge of metadata management and tagging schemas.
Understanding of data security classifications and controlled access in Lakehouse environments.
Strong communication skills to articulate technical concepts to non-technical stakeholders.
Responsibilities
Design logical and physical models for unstructured data from KM pipelines.
Define ownership and boundaries for various data products.
Establish consistent metadata standards and classification taxonomies.
Enforce security classifications compliant with governance and legal standards.
Register and maintain data products within Databricks Unity Catalog.
Collaborate with data engineers to align ingestion and transformation with data models.
Partner with various stakeholders to ensure data product usability and alignment with needs.
Document repeatable modeling standards to streamline future product onboarding.
Benefits
Remote work flexibility in the USA.
Opportunities for professional growth within the newly formed KM Data Platform team.
Involvement in crucial data governance and privacy initiatives.
Be a foundational member setting standards for future knowledge management data initiatives.
Full Job Description
Job Description
Role: Data Modeler Location: Remote USA Employment type: Fulltime
Job Overview:
The Senior Data Modeler will design and govern the data architecture for unstructured knowledge assets across 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 formed KM Data Platform team supporting broader Knowledge and Research Systems strategy.
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.
Qualifications
Must have skills:
Data Modeling (Strong), Databricks.
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.
Good to have skills:
BI Schema Design - General Experience.
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.
