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Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Interviewed for a data engineer role at Meta. The content was pretty thin so I'm not entirely sure what round this was, but it seemed to be centered around product analytics concepts using Instagram Likes as the core case.

Questions Asked (1)

Q1

How would you define and measure the success of Instagram Likes as a product feature?

Product Analytics & MetricsProduct Sense & Ideation
Author's notes

This is a classic Meta-style metrics question and I probably overthought it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the purpose of Instagram Likes: to express appreciation, provide social validation, and signal content quality. Then define success through a hierarchy of metrics (engagement, retention, well-being) and propose a measurement plan that balances growth with user well-being.

Pro tip: Acknowledge the tension between engagement and well-being—Meta has publicly grappled with this—and propose guardrail metrics to show you understand the broader product ecosystem.

1. Clarify the feature's purpose

Explain that Likes serve multiple purposes: for users, they provide social validation and feedback; for the platform, they signal content quality and drive engagement. This sets the foundation for defining success.

2. Define success metrics

Propose a mix of metrics: engagement (likes per user, like rate), retention (impact on DAU/MAU), and well-being (surveys on self-esteem, anxiety). Prioritize metrics based on product goals.

3. Outline measurement methods

Suggest A/B testing to isolate the impact of Likes (e.g., hide likes for a subset), cohort analysis to track long-term effects, and qualitative user research to capture sentiment.

4. Address trade-offs and guardrails

Discuss potential negative effects like social comparison and anxiety. Propose guardrail metrics (e.g., reports of bullying, user well-being scores) to ensure changes don't harm users.

5. Conclude with a balanced scorecard

Summarize by recommending a balanced scorecard that includes engagement, retention, and well-being metrics, and emphasize the need for continuous monitoring and iteration.

Key Points to Mention

  • Engagement metrics: likes per user, like rate, time spent
  • Retention and growth metrics: DAU/MAU, churn, new user activation
  • Well-being metrics: self-esteem surveys, anxiety levels, social comparison
  • A/B testing and holdout groups to measure causal impact
  • Guardrail metrics to prevent negative side effects
  • Qualitative research to understand user sentiment and motivations

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