Metrics Analysis and Data Driven Problem Solving Questions

Skills for using quantitative metrics to diagnose and solve product or support problems. Candidates should be able to identify relevant key performance indicators such as customer satisfaction, response time, resolution rate, and first contact resolution, detect anomalies and trends, formulate and prioritize hypotheses about root causes, design experiments and controlled tests to validate hypotheses, perform cohort and time series analysis, evaluate statistical significance and practical impact, and implement and monitor data backed solutions. This also includes instrumentation and data collection best practices, dashboarding and visualization to surface insights, trade off analysis when balancing multiple metrics, and communicating findings and recommended changes to cross functional stakeholders.

EasySystem Design

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Design a one-page operational dashboard for a support organization to monitor day-to-day health. List key KPIs, charts, top filters, required drilldowns, and at least three guardrail metrics you would include to detect regressions quickly.

HardTechnical

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Write an efficient SQL query (Postgres or BigQuery) to compute 30-day rolling retention per signup cohort and return the top five cohorts with the steepest positive retention slope. Assume large-scale data; explain any optimizations (partitioning, pre-aggregation) you would use to make this run in production.

EasyTechnical

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Define seasonality in time series for product metrics and describe two practical methods you would use to account for weekly and monthly seasonality when analyzing Year-over-Year changes in Weekly Active Users (WAU).

MediumTechnical

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You observe a measurable 10% decrease in checkout conversion this week. List at least six plausible hypotheses explaining the drop (across product, data, traffic, and business causes). Then prioritize them by ease and impact and describe one concrete data query or check for each hypothesis to validate or rule it out.

MediumTechnical

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Define Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for a support ticketing system focused on response and resolution. Propose three alerting thresholds that would escalate from an on-call page to a stakeholder email, and explain how to set each threshold to minimize alert fatigue.

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