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.
MediumTechnical
0 practiced
Explain Slowly Changing Dimension (SCD) Type 2 and why it's important for analytics that track attribute history (e.g., customer tier changes). Describe a dbt-oriented implementation pattern (models, snapshots, keys) to maintain historical dimensional data and make it accessible to BI dashboards.
HardSystem Design
0 practiced
Design an analytics platform that supports both interactive analyst SQL and self-serve BI dashboards for ~1,000 analysts and ingestion of 100M events/day. Outline choices for data warehouse (storage/compute separation), OLAP engines, semantic layer, access control, metadata catalog, workload isolation, and cost controls to support scale and governance.
HardTechnical
0 practiced
Compare Lambda, Kappa, and purely batch architectures for analytics. For a fintech product requiring strict correctness, sub-minute updates, and complex enrichment joins, which architecture would you recommend and why? Discuss operational complexity, reprocessing needs, and cost trade-offs.
HardTechnical
0 practiced
Design incremental aggregation tables to support interactive dashboards with ~100ms response targets for top KPIs (e.g., DAU, revenue by segment). Describe partitioning strategy, aggregation frequencies, incremental update mechanisms (streaming vs batch upserts), storage formats, and how to integrate with BI caching to achieve low-latency responses.
EasyTechnical
0 practiced
Explain the key differences between ETL and ELT. For a company using a cloud data warehouse (Snowflake or BigQuery) that ingests product event streams and transactional data, which approach would you recommend as a Business Intelligence Analyst and why? Discuss trade-offs around compute cost, data freshness, schema flexibility, and how downstream analytics (dashboards, ML features) are affected.
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