Staff Engineer - Data Modeler
RemoteCanada
Job Summary
Design logical and physical data models for unstructured and semi-structured content originating from KM pipelines such as case mining and informal knowledge capture workflows. Define domain boundaries and ownership for data products, establishing metadata standards and tagging taxonomies 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 to ensure ingestion and storage patterns align to the modeled domain structure. Collaborate with Knowledge Products, Research Products, and Architecture stakeholders to align data product design with downstream consumption needs. Establish and document repeatable modeling standards and playbooks so future data products can be onboarded consistently as the KM platform scales.
Required Qualifications
- 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
- 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
Desired Qualifications
- Hands-on experience with a modern data catalog; Databricks Unity Catalog experience strongly preferred
- 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
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