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
47 practiced
Describe how you'd detect silent data corruption in analytics (for example, an off-by-one bug in a daily transform that inflated counts for several weeks). Provide a monitoring and testing strategy, what automated checks you'd run, and a plan to correct historical data while preserving auditability for stakeholders.
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.
HardSystem Design
50 practiced
Design a data contract and schema versioning system for event producers so downstream analytics consumers (BI and ML) are not broken by changes. Describe enforcement mechanisms (CI checks, schema registry), a contract registry, deprecation policies, and how the BI team participates in contract governance and testing.
MediumTechnical
43 practiced
Compare a centralized analytics platform with a data mesh approach for a company of ~500 engineers and multiple product domains. From a BI Analyst's perspective, what are the implications for discoverability, metric consistency, ownership, and governance? Which approach would you recommend and why?
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