Data Driven Analysis and Optimization Questions

Using data to diagnose problems, prioritize experiments, and drive optimizations. Includes clarifying metrics and goals, identifying and gathering relevant data, analyzing trends and anomalies, forming testable hypotheses, designing experiments such as A B tests, interpreting statistical significance, distinguishing correlation from causation, and recommending actions based on insights. Interviewers look for structured analytic workflows, comfort with basic statistics, and the ability to translate analysis into measurable product or operational improvements.

HardTechnical

28 practiced

A/B experiment has issues with bot traffic inflating metrics. As a Data Engineer, describe a strategy to detect and mitigate bots in historic event data and in the live stream for future experiments. Include filtering heuristics, deterministic rules, ML signals, and how to avoid introducing bias when filtering.

HardTechnical

30 practiced

You're designing an experiment-analysis job that computes user-level uplift for multiple metrics. Some metrics are binary, some continuous, and revenue is heavy-tailed. Describe how you would choose statistical tests or estimators for each metric type, and how you would handle multiple comparisons when testing many metrics.

MediumTechnical

37 practiced

A data scientist proposes using conversion per session as the primary metric for a feature, but the product team prefers conversion per user. As a data engineer, how would you evaluate which metric is more robust and what infrastructure/support would you provide to ensure analyses are comparable (e.g., aggregation granularity, dedup keys)?

MediumTechnical

32 practiced

Create a reproducible playbook (step-by-step) that the data engineering team can follow when an experiment shows 'statistically significant' negative impact on a guardrail metric (e.g., login success rate). Include immediate mitigation steps, communications, rollback criteria, and postmortem data checks.

EasyTechnical

57 practiced

Create an example JSON schema for an experiment telemetry event that captures assignment information and exposure metadata necessary for accurate analysis. Include fields for experiment_id, variant_id, user_id, device_id, cohort, exposure_time, request_id, and context. Explain why each field is important and which are required vs optional.

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