Staff Engineer - Data Engineer

We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale — across all devices and digital mediums, and our people exist everywhere in the world (15000+ experts across 26 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!

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 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.

Must have skills: Data Modeling (Strong), Databricks Good to have skills: BI Schema Design - General Experience