End to end topic covering the precise definition, computation, transformation, implementation, validation, documentation, and monitoring of business metrics. Candidates should demonstrate how to translate business requirements into reproducible metric definitions and formulas, choose aggregation methods and time windows, set filtering and deduplication rules, convert event level data to user level metrics, and compute cohorts, retention, attribution, and incremental impact. The work includes data transformation skills such as normalizing and formatting date and identifier fields, handling null values and edge cases, creating calculated fields and measures, combining and grouping tables at appropriate levels, and choosing between percentages and absolute numbers. Implementation details include writing reliable structured query language code or scripts, selecting instrumentation and data sources, considering aggregation strategy, sampling and margin of error, and ensuring pipelines produce reproducible results. Validation and quality practices include spot checks, comparison to known totals, automated tests, monitoring and alerting, naming conventions and versioning, and clear documentation so all calculations are auditable and maintainable.
MediumTechnical
0 practiced
Write SQL that computes a metric's numerator and denominator for two consecutive months and returns absolute change and percent change. Use orders(order_id, user_id, amount, occurred_at) and handle NULL denominators gracefully (e.g., return NULL or an explanatory value). Provide the SQL snippet and explain edge-case handling.
HardTechnical
0 practiced
An upstream data source started excluding a user segment yesterday, causing a core retention metric to drop. Outline how you would quantify the impact (what queries, comparisons, and back-of-envelope checks you would run), coordinate a fix with engineering, design a remediation/backfill plan, and communicate the issue, impact, and timeline to executives including uncertainty estimates.
HardTechnical
0 practiced
Design transformations in a streaming framework (Apache Beam or Spark Structured Streaming) to compute session-based metrics from event streams with out-of-order events: sessions per user, average session length, and number of sessions per day. Explain window choices, watermarking, late data handling, stateful sessionization, and deduplication approaches.
MediumTechnical
0 practiced
You must choose between COUNT(DISTINCT user_id) and approximate distinct algorithms like HyperLogLog (HLL) for unique-user counts in dashboards. Explain the trade-offs in accuracy, memory and compute cost, mergeability across partitions, and scenarios where HLL is appropriate. Provide thresholds or heuristics you would use in a production environment.
HardTechnical
0 practiced
Two product teams disagree about the canonical definition of 'Active User' (team A: any event in 30 days; team B: product-critical events in 30 days). As the BI lead, describe your process to resolve the disagreement, set a canonical definition, document exceptions, and implement governance so dashboards use the canonical metric by default. Include stakeholder engagement and rollback plans.
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