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.

MediumTechnical

0 practiced

You're implementing daily_active_users in LookML across mobile and web where user identifiers differ by platform. Explain how you'd model a canonical user_id (stitching), build persistent derived tables (PDTs) to calculate DAU efficiently, manage timezone normalization, and expose the metric to explores and dashboards while ensuring consistent definitions.

MediumTechnical

0 practiced

A checkout optimization increases conversion but reduces average order value (AOV). Outline an analytical plan to quantify the net revenue impact, including per-user vs per-order metrics, statistical tests or bootstrap methods to compute confidence intervals, segment-level analysis, and decision rules you would recommend to product stakeholders.

MediumTechnical

0 practiced

A proposed feature increases signup conversion by 8% but increases support cost per user by 15%. Propose a quantitative framework to evaluate whether to ship: estimate incremental LTV, compute payback period, run sensitivity analysis on key assumptions, and propose KPIs to monitor after launch.

EasyTechnical

0 practiced

You see a 50% overnight drop in reported daily resolved tickets on the executive dashboard. Provide a prioritized checklist to determine whether this is a real business issue or a reporting/data problem. Include fast SQL checks, instrumentation checks, ETL and pipeline health checks, and quick visualizations you would run.

MediumSystem Design

0 practiced

Design an ETL pipeline to support near-real-time SLA dashboards with max 5-minute latency. Describe ingestion, transformation, storage (OLTP vs OLAP choices), how to handle late-arriving events, idempotency, and trade-offs between cost and query freshness.

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