Enterprise Context Architect

Role Description

Dropbox is building the knowledge layer that connects content, context, and action. As AI moves from assistants to systems that act, the structure and stewardship of enterprise knowledge becomes the difference between AI that helps and AI that fails.

This role owns the context layer our AI depends on: what it can know, what it can trust, and what it is allowed to act on. You will lead the central function, setting the strategy, architecture, standards, and operating model that make enterprise knowledge reliable, current, and permissions-aware for both human and AI use, while domain experts stay accountable for the accuracy of their content.

AI capability changes quickly, and this role changes with it. What a model can interpret, how content needs to be structured for retrieval, and what a system can safely act on all shift as the technology moves. You will track those shifts, translate them into practical standards, and revise your own past decisions when the ground moves under them.

This is the first role of its kind at Dropbox. You will partner with IT, Engineering, Legal, Privacy, and Security, and your decisions will show up directly in how AI performs across the company.

Responsibilities

Define the source-of-truth strategy for enterprise knowledge: which systems are authoritative, what is indexed centrally versus fetched live, what is eligible for AI use, and what is archived or excluded, informed by an assessment of the authoritative sources behind our highest-value workflows.

Define the enterprise standards that make content AI-ready across structure, metadata, provenance, and access, including where semantic models or knowledge graphs are warranted and where they are not, and translate them into authoring patterns adopted across domains.

Design the control model for AI actions, including eligibility rules, preconditions, approval boundaries, escalation paths, and rollback requirements, so systems that act on enterprise knowledge stay traceable and safe as AI capabilities evolve.

Lead platform and connector strategy across the content stack. Drive decisions on what is refactored, migrated, indexed in place, or consolidated, and partner with IT and Engineering on connector architecture and how AI systems are granted access to tools and sources.

Build the federated operating model for enterprise content: stewardship across functions, domains accountable for their own accuracy within shared standards, and lifecycle policies covering review cadence, expiration, material-change triggers, and retirement, tied to business criticality.