Data Driven Decision Making Questions

Using metrics and analytics to inform operational and strategic decisions. Topics include defining and interpreting operational measures such as throughput cycle time error rates resource utilization cost per unit quality measures and on time delivery, as well as growth and lifecycle metrics across acquisition activation retention and revenue. Emphasis is on building audience segmented dashboards and reports presenting insights to influence stakeholders diagnosing problems through variance analysis and performance analytics identifying bottlenecks measuring campaign effectiveness and guiding resource allocation and investment decisions. Also covers how metric expectations change with seniority and how to shape organizational metric strategy and scorecards to drive accountability.

MediumSystem Design

33 practiced

Design an executive KPI scorecard for a product team covering acquisition, activation, retention, and revenue. Provide 6–8 KPIs, concise definitions/formulas for each, the data source for each metric, and green/amber/red thresholds with a one-sentence rationale for each threshold.

HardTechnical

32 practiced

Your nightly ETL window currently takes 8 hours but the business requires dashboards updated within 1 hour after midnight. Propose a detailed optimization plan including CDC (change data capture), incremental transforms, partition pruning, parallel processing, materialized views, and an approach to estimate cost trade-offs on cloud providers.

MediumTechnical

29 practiced

You observe a 20% week-over-week drop in conversion. Walk through a structured variance analysis you would perform as a BI Analyst: include data validation steps, segmentation to isolate affected cohorts, funnel checks, seasonality adjustments, deployment/experiment checks, and how you'd prioritize hypotheses to investigate.

EasyTechnical

44 practiced

Write a PostgreSQL query to compute Monthly Active Users (MAU) for the last 6 months given the table:

events(
  event_id bigint PRIMARY KEY,
  user_id bigint,
  event_type text,
  event_ts timestamp with time zone
)

Return rows with columns: month (YYYY-MM) and mau_count. Use calendar months and ensure each user is counted once per month. Explain any timezone assumptions.

HardTechnical

33 practiced

Define a metric and modeling approach for campaign ROI that includes hidden costs (creative, implementation, analytics) and accounts for time-lagged conversions. Describe lookback/attribution windows, survival / hazard modeling for delayed conversions, and statistical pitfalls like selection bias and cannibalization.

Unlock Full Question Bank

Get access to hundreds of Data Driven Decision Making interview questions and detailed answers.

Join thousands of developers preparing for their dream job.