Staff Engineer Data Engineer

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.

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 612 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: Data Modeling (Strong) Databricks

Good to have skills: BI Schema Design - General Experience

Remote Work :

No

Employment Type :

Full-time