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Meta·Software Engineer·Onsite - Product Sense / Strategy·Senior

Senior
May 2026

Summary

Did a product design interview at Meta and got asked about advocacy within the design process. Pretty short but it made me think harder than I expected.

Questions Asked (1)

Q1

Tell me about a feature you pushed to keep in a product and why you believed in it.

Product Sense & IdeationStakeholder ManagementRoadmap Prioritization
Author's notes

This one tripped me up a bit.

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AI HintsAI Generated

Suggested Approach

Choose a feature where you had a clear product vision and data-backed conviction, and you successfully navigated pushback from stakeholders. Structure your answer to show how you balanced user value, business impact, and technical feasibility to make a compelling case.

Pro tip: Emphasize how you validated your belief with data or user research, and how you remained open to feedback while standing firm on the core value—this shows both conviction and collaboration.

1. Set the Context

Briefly describe the product, the feature, and the situation that led to the debate about whether to keep it. Explain why it was controversial.

2. Articulate Your Belief

Clearly state why you believed in the feature, focusing on user needs, data, or strategic alignment. Avoid emotional arguments; ground your reasoning in evidence.

3. Show How You Advocated

Describe the actions you took to persuade stakeholders, such as presenting data, running experiments, or building a prototype. Highlight collaboration and listening to concerns.

4. Share the Outcome

Explain the result: did the feature stay? What impact did it have on users and metrics? If it was later removed, what did you learn?

5. Reflect and Connect

Summarize the key lesson about product sense or stakeholder management, and relate it to the role at Meta.

Key Points to Mention

  • User impact and how you measured it (e.g., retention, engagement)
  • Data or research that supported your belief
  • Stakeholder concerns and how you addressed them
  • Trade-offs considered (e.g., technical debt, opportunity cost)
  • The decision-making process and who was involved
  • The final outcome and what you learned

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