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.
HardTechnical
0 practiced
You observe exploding cloud costs for ETL jobs running on transient clusters. Propose a systematic cost-optimization plan for petabyte-scale ETL workloads covering compute sizing, spot/commit strategies, storage lifecycle policies, compaction cadence, and query optimization tactics that preserve throughput SLA.
MediumTechnical
0 practiced
Your company must train models using customer PII but must also comply with GDPR and CCPA. Describe concrete data governance and privacy controls you would implement across ingestion, storage, training pipelines, and model artifacts to align technical practice with legal requirements while minimizing hindrance to model development.
EasyTechnical
0 practiced
Describe schema-on-read and schema-on-write paradigms. As an AI Engineer, when would you prefer schema-on-read for model training data, and what risks does that introduce for data quality and reproducibility? Give two concrete mitigation strategies.
EasyTechnical
0 practiced
Given this simple event table schema for a web product, write an SQL query (ANSI SQL) to compute weekly active users (WAU) per product category for the last 8 weeks. Schema:Return rows: week_start, category, wau. Describe assumptions about timezones and late-arriving events.
events(event_id bigint, user_id bigint, product_id bigint, category varchar, event_ts timestamp)EasyTechnical
0 practiced
Explain the difference between ETL and ELT in the context of AI-driven products. Describe situations where you would choose ELT over ETL when building training pipelines for large-scale models, considering data volume, compute locality, transformation complexity, and downstream analytics needs. Include a short example workflow for each approach.
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