Design the event instrumentation for Creator Mode at scale

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 Creator Mode changes how professionals, including founders, present their identity, build an audience, publish content, and convert profile visitors into followers, connections, customers, hires, or investors. You are asked to design the event instrumentation needed to understand how Creator Mode is being discovered, enabled, used, and producing value at LinkedIn scale.

Focus on the technical product management aspects of instrumentation rather than redesigning Creator Mode itself. The system must support reliable product analytics, experimentation, funnel diagnosis, creator lifecycle tracking, and downstream reporting while respecting LinkedIn’s standards for professional trust, privacy, identity quality, and data governance.

Consider the full workflow: a founder discovers Creator Mode, turns it on or off, configures profile topics and creator settings, publishes or engages with content, attracts profile visits and followers, and receives feedback through analytics or notifications. Your instrumentation should make these journeys measurable across surfaces such as profile, feed, notifications, search, mobile, and web.

The experience should consider:

- Core user actions and system events that need to be captured across the Creator Mode lifecycle.

- Event schemas, required properties, identifiers, timestamps, source surfaces, and versioning needs.

- How to distinguish creators, viewers, followers, connections, founders, and other relevant cohorts.

- Data quality concerns such as duplicate events, missing events, delayed delivery, client/server discrepancies, and bot or spam activity.

- Privacy, consent, access control, retention, and sensitive professional identity considerations.

- APIs, logging pipelines, analytics consumers, dashboards, and experimentation systems that may depend on the events.

- Reliability, observability, alerting, backfills, and rollout strategy for instrumentation at LinkedIn scale.

- Product trade-offs between instrumentation depth, engineering cost, latency, user privacy, and decision usefulness.

Your goal is to define a clear, scalable instrumentation approach that enables LinkedIn teams to measure Creator Mode adoption, engagement, quality, and business impact without compromising trust, performance, or data integrity.