Metric Definition and Implementation Questions

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

Streaming implementation: Provide pseudocode (PySpark/Scala) for deduplicating events and sessionizing user events in Spark Structured Streaming. Discuss watermark choices, state TTL, how to scale state for millions of users, and trade-offs of stateful processing versus micro-batching.

HardTechnical

0 practiced

Optimization: Provide an approach and sample SQL to compute cohort retention using materialized views or incremental tables so you avoid recomputing large joins nightly. Explain schema design for incremental maintenance, refresh strategy, and trade-offs in staleness versus compute cost.

HardSystem Design

0 practiced

Design a metric registry and reproducible metrics platform. Requirements: store SQL metric definitions with versioning and lineage, maintain 1,000 metrics, support backfills, audit logs, and access control. Sketch components (metadata store, catalog, compute engine, serving layer), dataflows, and how you would ensure reproducible materialization.

EasyTechnical

0 practiced

Define what a business metric is and how it differs from a dimension and a KPI. Give concrete examples mapping event-level data to a metric versus a dimension (for example: page_view_count, user_country, conversion_rate). Explain when a metric should be computed at event-level versus aggregated, and name one case where computing at the wrong level causes bias.

MediumSystem Design

0 practiced

Describe how to decide between computing metrics on demand from raw events, scheduled batch aggregates, or maintaining materialized views. Discuss factors including query patterns, freshness requirements, cost (compute vs storage), cardinality, and ease of maintenance.

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