Data Analysis and Requirements Translation Questions
Focuses on translating ambiguous business questions into concrete data analysis plans. Candidates should identify the data points required, define metrics and key performance indicators, state assumptions to validate, design the analysis steps and queries, and explain how analysis results map back to business decisions. This includes data quality considerations, required instrumentation, and how analytical findings influence product requirements or architectural choices.
EasyTechnical
0 practiced
Explain why versioning metric definitions and storing historical versions of key SQL queries and ETL transforms is important. Describe a practical approach for versioning metric specs, exposing versions to analysts, and preventing silent metric breakage across dashboards when the implementation changes.
HardTechnical
0 practiced
Propose a canonical, repeatable process that maps high-level business goals to operational KPIs. Include who should participate (PM, data scientist, data engineer, legal), artifacts to produce (metric spec, instrumentation checklist, dashboards), decision gates (experiment vs immediate rollout), and guardrails to avoid metric manipulation or conflicting incentives.
HardSystem Design
0 practiced
Design an access control and auditing model for a metrics platform so metric definitions, raw datasets, transformed artifacts, and transformation code have role-based permissions, approval workflows for changes, and immutable audit logs. Explain how to minimize over-privileged access while enabling analysts discoverability and self-service.
MediumSystem Design
0 practiced
Describe how you would design platform features to enable reproducible analyses: capture immutable raw datasets, version transformation code and SQL, snapshot intermediate artifacts, store environment metadata, and present lineage. Explain how analysts can re-run historical analyses deterministically and what trade-offs exist around storage and retrieval costs.
HardSystem Design
0 practiced
Your organization plans to migrate several daily batch metrics to streaming to achieve 1-minute freshness. Outline a migration plan: which metrics to migrate first, how to run batch and streaming in parallel, methods to validate parity between the two systems, strategies for historical recomputation/backfill, and monitoring you would put in place during migration.
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