Customer and User Obsession Questions

Demonstrating a deep commitment to understanding and advocating for customers and end users. Candidates should show how they prioritize user needs in decision making, even when it conflicts with other priorities, and provide concrete examples of advocating for users internally. Topics include using qualitative and quantitative research to surface user pain points, validating assumptions with user evidence, designing or improving experiences to solve real problems, maintaining ongoing connection to users through feedback loops, and influencing stakeholders to keep the organization user focused. Examples may range from entry level empathy and direct customer learning to strategic changes driven by user insight.

MediumTechnical

0 practiced

What data observability checks would you implement to ensure product metrics are trusted by PMs and execs? Rank the top five checks you would implement first for user-facing metrics and explain why each is important (e.g., freshness, drift, completeness).

MediumTechnical

0 practiced

You observe a sudden drop in funnel conversion for mobile users beginning at 02:00 UTC. Raw events show many missing device IDs and session_ids. Walk through a triage plan: immediate mitigations to reduce user impact, data checks to identify root cause, and long-term fixes to prevent recurrence.

HardTechnical

0 practiced

A streaming schema changed: field `device_os` was renamed to `client_os` across a rolling deployment, and some producers still emit the old field. Design a robust ETL transformation (Spark Structured Streaming pseudocode and schema-mapping strategy) that supports both field names, handles backfills, and allows a safe migration to the new schema without downtime.

MediumBehavioral

0 practiced

Describe a time when you simplified complex analytics so product managers could make user-focused decisions faster. What was the complexity, what simplification did you implement, how did you measure success, and what did you learn?

HardSystem Design

0 practiced

Architect a scalable experimentation metrics pipeline that ensures consistent metric definitions across offline and real-time dashboards, supports 500 concurrent experiments per day, 100M users, and permits retroactive re-computation of metrics when instrumentation bugs are fixed. Outline core components, consistency model, backfill strategy, and how you guarantee experiment integrity.

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