What API and data model would support a new Messaging workflow for hiring managers

Problem Statement Description

Product context: LinkedIn is Microsoft's professional network; its products include profiles, feed, jobs, recruiting, LinkedIn Learning, sales tools, messaging, and ads.

LinkedIn wants to support a new Messaging workflow tailored to hiring managers who need to communicate with candidates, recruiters, interviewers, and possibly agency or talent partners during the hiring process. The workflow should fit into LinkedIn’s professional network context, where member identity, privacy expectations, relationship context, and recruiting intent all affect how messages are created, routed, displayed, and governed.

In this technical PM question, you are asked to define the API surface and data model needed to support this workflow. Focus on what entities, relationships, permissions, events, and system interactions are required for a hiring-oriented messaging experience, rather than designing the full UI or choosing a specific database technology.

Your response should account for both product needs and platform constraints: reliable message delivery, participant roles, candidate privacy, recruiter collaboration, integration with hiring workflows, and observability for debugging and iteration. Assume the system may need to interoperate with existing LinkedIn Messaging, Recruiter, Jobs, notifications, and member profile data.

The experience should consider:

- Core users and roles, such as hiring managers, recruiters, candidates, interviewers, and admins

- Conversation structure, including one-to-one threads, group threads, candidate-linked conversations, and hiring-process context

- API requirements for creating, reading, sending, updating, searching, archiving, and permission-checking messages or threads

- Data model entities such as participants, messages, attachments, job or candidate references, read states, status, audit history, and metadata

- Privacy, consent, access control, retention, compliance, spam prevention, and professional trust safeguards

- Reliability and scale expectations, including idempotency, ordering, retries, rate limits, notifications, and failure handling

- Observability needs, including logs, delivery states, error tracking, analytics events, and operational dashboards

- Product trade-offs between speed of implementation, reuse of existing Messaging infrastructure, recruiting-specific customization, and long-term extensibility

The goal is to evaluate how you translate a hiring manager messaging workflow into clear technical requirements, APIs, and data structures while balancing user experience, platform integrity, privacy, and operational reliability in LinkedIn’s ecosystem.