Design an internal interest-matching platform

Quick Overview

Design an internal interest-matching platform evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Design an internal interest-matching platform

Company: Palantir

Role: Software Engineer

Category: System Design

Difficulty: hard

Interview Round: Technical Screen

Design a system for a company to connect employees who share similar interests. Specify key features (profile creation, interest tagging, discovery, messaging), data model and storage choices, APIs, and the matching/recommendation approach. Discuss privacy and access controls for internal-only visibility, ranking signals, and how to handle cold start. Provide a high-level architecture (services, databases/search indices, cache, event/queue) and describe how it scales to tens of thousands of employees with near real-time updates as interests change. Outline monitoring/metrics and abuse prevention.

Overview: Design an internal interest-matching platform evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Palantir
Jul 15, 2025
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Design an internal interest-matching platform

System Design Prompt: Internal Employee Interest Matching Platform

Context

Design an internal-only platform that connects employees with similar interests to foster collaboration, mentorship, and community. Assume a typical enterprise environment with SSO, an HR directory, and tens of thousands of employees. The system should provide near real-time updates when interests change.

Requirements

  • Features
    1. Profile creation and editing
    2. Interest tagging (free text with normalization and a suggested taxonomy)
    3. Discovery and search (by tags, location, team, etc.)
    4. Matching/recommendations (who should I connect with?)
    5. Messaging (1:1 and small group)
  • Data model and storage choices
  • APIs (profile, interests, discovery, recommendations, messaging)
  • Matching and ranking approach (signals, algorithm)
  • Privacy and access controls (internal-only visibility, role/attribute-based)
  • Cold start strategy
  • High-level architecture (services, databases/search indices, cache, event/queue)
  • Scaling plan to tens of thousands of employees with near real-time updates
  • Monitoring/metrics and abuse prevention

Clarifying Questions to Ask Guidance

  • Clarify users, core use cases, read/write patterns, scale, latency, availability, and data retention.
  • State explicit assumptions before making sizing or architecture decisions.
  • Prioritize the functional path first, then address reliability, security, observability, and rollout.

What a Strong Answer Covers Guidance

  • A scoped requirements summary with concrete non-goals and success metrics.
  • API, data model, architecture, consistency, capacity, and operations.
  • Reasoned trade-offs among simple and scalable designs, including bottlenecks and failure modes.
  • A validation, monitoring, migration, and launch plan appropriate for the risk level.

Follow-up Questions Guidance

  • What breaks first at 10x traffic or data volume?
  • How would you degrade gracefully during dependency failures?
  • What metrics and alerts would prove the design is healthy after launch?

Submit Your Answer to Earn 20XP

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