Company Product and Design Knowledge Questions

Demonstrate a well researched understanding of the company, its major products, target users, market position, and core business model, combined with familiarity with the company design philosophy and visible product design patterns. Prepare to speak about flagship products and features, typical user demographics and needs, the engineering or product challenges the company faces, and how those constraints shape product and design decisions. For design roles, be ready to articulate what you admire about the company design aesthetic, specific patterns or interactions you observe, accessibility and usability trade offs, and how your own design sensibilities or past work align with and could contribute to that aesthetic. For non design roles, emphasize product priorities, technical or operational challenges, and how your skills would help advance those products. Cite concrete examples such as a recent feature, a product workflow, a known engineering challenge, or public design documentation to show you have done focused research.

EasyTechnical

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For the company you are interviewing with, pick three flagship features (use public evidence). For each feature explain: the primary user persona it serves, the business goal it supports, two quantitative metrics you would track to measure success, and one specific visualization or chart you would include in a product dashboard to monitor that feature.

HardSystem Design

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Design an alerting and monitoring framework for product and design dashboards that reliably surfaces major regressions such as sudden drops in cohort retention, spikes in UI error events, or large shifts in completion rate. Explain thresholding, anomaly detection approach, escalation channels, and methods to reduce false positives and alert fatigue.

MediumBehavioral

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Behavioral: Describe a time you used data to push back on a product or design decision you believed would harm users. Explain the analysis you ran, how you communicated results to designers or PMs, any compromises reached, and the final outcome.

MediumTechnical

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Some product events include a JSON properties field with dynamic metadata. Discuss strategies to model and flatten semi-structured event properties in a data warehouse for reliable metric computation. Compare options such as normalized tables, JSON functions (parsing at query time), materialized views, or exploding properties at ingestion, and explain trade-offs for query performance and dashboard freshness.

MediumTechnical

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Explain three ways in which product design constraints such as small mobile screens, slow network conditions, or battery optimization priorities change which product metrics a BI analyst should prioritize. Provide a concrete example for each constraint showing how metric definitions or visualizations should change.

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