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.

MediumTechnical

0 practiced

Describe how you would compute a market share metric by joining two data sources: internal sales and an external market dataset that reports aggregate market volume by week. Explain deduplication, matching keys, currency/price normalization, and how to handle missing weeks or mismatched granularity.

HardSystem Design

0 practiced

Design a robust backfill and reprocessing strategy for a dataset when you discover a bug in event parsing that affected six months of data. Requirements: ensure reproducibility, maintain reproducible audit logs, minimize downtime for dashboards, and provide a migration plan for dependent metrics. Explain how to coordinate with engineering and stakeholders.

MediumTechnical

0 practiced

Design a set of automated tests for SQL metric definitions. Include examples of unit tests (small synthetic datasets), integration tests (end-to-end pipeline validation), and data-contract tests (schema, nullability, cardinality). Describe the tooling you would use (dbt tests, Great Expectations, pytest) and how tests are integrated into CI/CD.

EasyTechnical

0 practiced

Explain three common deduplication strategies for event-level data (SQL or ETL): 1) simple order_id dedup, 2) session-based dedup using event timestamps, and 3) probabilistic dedup across devices. For each strategy, describe when it is appropriate, its limitations, and an example SQL/algorithmic approach.

MediumSystem Design

0 practiced

Design monitoring and alerting for a metrics pipeline that produces daily revenue and DAU. Describe what to monitor (volumes, schema drift, row counts, null rates, distribution changes), how to set thresholds and SLOs, which tools you'd use (datadog, prometheus, Great Expectations, etc.), and how to route alerts to appropriate teams with on-call playbooks.

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