Staff Engineer – Data Modeler

This is a fully remote position, open to applicants in Canada.

📋 Description

• Create both logical and physical data models for unstructured and semi-structured content derived from Knowledge Management pipelines.

• Define the boundaries and ownership of domains for reusable data products as opposed to raw or intermediate assets.

• Establish standards for metadata and tagging taxonomies across various knowledge sources.

• Assign and enforce classifications of security and sensitivity in accordance with data governance, privacy, and legal/risk requirements.

• Register, document, and manage data products in Databricks Unity Catalog, including schemas, access permissions, lineage, and catalog metadata.

• Collaborate with data engineers to ensure that ingestion, transformation, and storage patterns align with the structured domain models.

• Work alongside stakeholders from Knowledge Products, Research Products, and Architecture/Data/Technology to address downstream data product requirements.

• Assist in privacy and legal review processes by classifying and documenting data products.

• Develop repeatable modeling standards and playbooks for the onboarding of future data products.

• Deliver domain models, metadata taxonomies, registered Unity Catalog data products, and repeatable modeling standards within the initial 6–12 months.

⛳️ Requirements

• Proficient data modeling skills.

• Experience with Databricks.

• Over 5 years of experience in data modeling, data architecture, or information architecture.

• Significant exposure to unstructured or semi-structured data.

• Direct experience in or related to Knowledge Management, content management, or enterprise search.

• Practical experience with a modern data catalog; preference for Databricks Unity Catalog.

• Capability to define data domains and data product boundaries within a large, multi-stakeholder organization.

• Knowledge of metadata management, tagging schemas, taxonomies, controlled vocabularies, or ontology design.

• Understanding of data security and sensitivity classification frameworks and access control within a Lakehouse environment.

• Experience collaborating with data engineering teams on ingestion and pipeline design.

• Strong written and verbal communication skills.

• Familiarity with BI schema design.

• Experience with enterprise knowledge platforms such as Glean, SharePoint, or ServiceNow, or AI-driven retrieval systems.

• Knowledge of Databricks Delta Lake, Delta Sharing, or Lakehouse Federation.

• Prior experience in professional services, consulting, or a document/case-intensive knowledge environment.

• Familiarity with 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 mandatory.

🏝️ Benefits

• Comprehensive health and wellness benefits.

• Opportunities for professional development and growth.

• Flexible work arrangements and a supportive work environment.