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.

MediumSystem Design

128 practiced

Design an event ingestion pipeline to collect clickstream events at 100k events/sec sustained with burst capacity, low end-to-end latency for near-real-time dashboards, and 90-day raw event retention. List components, data flow, schema validation, backpressure strategy, and how you'd validate correctness during deployment.

EasyTechnical

87 practiced

Given a simple events table schema:

events(event_id STRING PRIMARY KEY, user_id STRING, event_type STRING, occurred_at TIMESTAMP)

Write an SQL query (ANSI SQL) that computes daily active users (DAU) per day and a 7-day rolling DAU (unique users over last 7 days) for each day. Explain assumptions about nulls, timezones, and deduplication.

HardTechnical

99 practiced

The product team asks you to define a single composite metric to represent 'user happiness' across product usage. How would you design such a composite metric? Describe potential components (e.g., retention, NPS, task-completion), weighting strategy, validation plan, and how you'd guard against gaming.

MediumTechnical

85 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

75 practiced

You detect bias in training data for a recommendation model: certain user groups have systematically fewer positive interactions. From a data engineering perspective, describe how you would detect, quantify, and mitigate this bias in data pipelines while coordinating with product and ML teams.

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