Data and Technical Strategy Alignment Questions
Assess how the candidates technical experience and perspective align with the companys data strategy, infrastructure, and product architecture. Candidates should demonstrate knowledge of the companys scale, data driven products, and technical tradeoffs, and then explain concretely how their past work, tools, and approaches would support the companys data objectives. Good answers connect specific technical skills and project outcomes to the companys announced or inferred data and engineering priorities.
EasyTechnical
40 practiced
What is a metrics (semantic) layer and why is it important for cross-functional consistency? As a BI Analyst, give concrete examples of how you'd implement or use a metrics layer (e.g., Looker explores, dbt models, a metric registry) to eliminate conflicting definitions across dashboards.
HardTechnical
43 practiced
Describe strategies to mitigate data skew or hot partitions in very large fact tables (e.g., 1 product or tenant accounts for most events). Discuss partitioning, clustering, salting, adaptive sharding, and query-side techniques to avoid performance degradation in BI queries.
HardTechnical
49 practiced
Plan a migration of 500 dashboards from Tableau to Looker (or from Looker to Power BI). Describe an end-to-end approach: inventory and classification, mapping calculations and joins, rewriting data models, validation of numbers, user training, phased rollout, and retirement of legacy assets. Call out major risks and mitigations.
HardTechnical
46 practiced
Propose a hybrid approach to join high-cardinality event streams with slowly changing dimension tables (user profiles) in a streaming pipeline so analytics are correct and costs remain reasonable. Discuss CDC for dims, state stores for enrichment, temporal joins, emitting corrections, and operational scaling.
EasyTechnical
41 practiced
Explain the differences between a star schema and a snowflake schema in a data warehouse. For BI dashboards that frequently slice metrics by user, product, and region, which schema would you choose and why? Discuss implications for query performance, analyst ergonomics, and maintenance overhead.
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