← Freshworks Interview Insights

Freshworks·Product Manager·Onsite - Product Sense / Strategy·Senior

Senior
May 2026

Summary

PM interview at Freshworks, one question about designing an analytics platform. Pretty open-ended, which I wasn't fully prepared for.

Questions Asked (1)

Q1

How would you design an analytics platform from scratch?

Product Sense & IdeationSystem DesignProduct Analytics & Metrics
Author's notes

Went broad first, probably too broad.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the business goals and target users of the analytics platform, then outline a phased approach covering data ingestion, storage, processing, and visualization. Emphasize how you would prioritize features based on customer needs and iterate using feedback and metrics.

Pro tip: Anchor your design in a specific customer problem or use case (e.g., 'helping support teams reduce churn by identifying at-risk accounts') to show product thinking, not just technical architecture.

1. Clarify Goals and Users

Ask questions to understand the platform's purpose, primary users (e.g., internal teams, external customers), and key business objectives. Define success metrics early.

2. Define Core Capabilities

Outline essential features: data collection, storage, processing, querying, visualization, and sharing. Prioritize based on user needs and technical feasibility.

3. Design Architecture

Propose a high-level architecture covering data sources, ingestion pipelines, storage layers (e.g., data lake, warehouse), processing engines, and APIs. Consider scalability, security, and cost.

4. Plan for Iteration and Metrics

Describe how you would launch an MVP, gather user feedback, and measure adoption and impact. Outline a roadmap for future enhancements.

Key Points to Mention

  • Data ingestion from multiple sources (APIs, databases, event streams)
  • Scalable storage and processing (e.g., data lake, warehouse, ETL/ELT)
  • Self-serve analytics and visualization for non-technical users
  • Security, privacy, and compliance (e.g., GDPR, SOC2)
  • Integration with existing products and workflows
  • Metrics for platform success (e.g., DAU, query volume, time-to-insight)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.