Staff Engineer - Data Engineer
HybridGuadalajara, Jalisco, Mexico
Job Summary
Design logical and physical data models for unstructured and semi-structured content originating from KM pipelines, establishing domain boundaries and ownership for discrete data products. Define metadata standards and tagging taxonomies to ensure consistent classification across knowledge sources, then assign and enforce security and sensitivity classifications aligned with firm data governance and legal requirements. Register and maintain these products in Databricks Unity Catalog, partnering with data engineers to ensure ingestion and storage patterns align with modeled structures. Collaborate with stakeholders to align designs with downstream consumption needs while supporting privacy and legal review processes through upfront documentation. Establish repeatable modeling standards and playbooks to enable consistent onboarding as the 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
- 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
- Databricks Unity Catalog experience strongly preferred
Desired 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
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