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.
HardTechnical
0 practiced
Write SQL to compute a weighted multi-touch attribution for conversions with a 7-day lookback where touch weights decay exponentially by days since touch. Schema: ad_touch(touch_id, user_id, touch_time TIMESTAMP, channel STRING), conversions(conversion_id, user_id, conversion_time TIMESTAMP, value NUMERIC). Output total attributed_value per channel. Provide the SQL (or pseudocode) and discuss optimizations for large datasets.
MediumTechnical
0 practiced
Some analytics SDKs sample events before ingestion. Explain how sampling affects metric accuracy, how to compute margin of error/confidence intervals for proportions and means when data is sampled, and what metadata you must store and show alongside metrics to indicate reliability to stakeholders.
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.
EasyTechnical
0 practiced
Explain the difference between event-level metrics and user-level metrics for product analytics. Give 2 examples of each, describe when you would prefer event-level over user-level, and outline a high-level approach to convert event-level data into user-level metrics in your ETL/ELT pipeline.
HardSystem Design
0 practiced
Design an enterprise-grade metrics catalog and pipeline that supports metrics-as-code, versioning, lineage, scheduled recalculation, immutable metric versions, and audit logs. Requirements: support ~1000 metrics, hourly and daily granularity, multi-region reads, ability to replay historical runs and rollback to prior metric versions. Describe architecture, storage formats, CI/CD for metrics, and how metrics are exposed to BI tools.
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