Data and Analytics Infrastructure Questions

Designing, building, and operating end-to-end data and analytics platforms that collect, transform, store, and serve event, product, and revenue data for reporting, analysis, and decision making. Core areas include event instrumentation and tag management to capture user journeys, marketing attribution, and experimental events; data ingestion strategies and connectors; extract-transform-load (ETL/ELT) pipelines and streaming processing; orchestration and workflow management; and the trade-offs between batch and real-time architectures. Candidates must be able to design storage and serving layers, including data warehouses, data lakes, lakehouse patterns, and managed analytical databases, and to choose storage formats, partitioning, and indexing strategies driven by volume, velocity, variety, and access patterns. Data modeling for analytics covers raw event layers, curated semantic layers, dimensional modeling, and metric definitions that support business intelligence and product analytics. Governance and reliability topics include data quality validation, freshness monitoring, lineage, metadata and cataloging, schema evolution, master data considerations, and role-based access control. Operational concerns include scaling storage, processing, and query concurrency; fault tolerance and resiliency; monitoring, observability, and alerting; and cost, performance, and capacity planning trade-offs. Finally, candidates should be able to evaluate and select tools and frameworks for orchestration, stream processing, and business intelligence; integrate analytics platforms with downstream consumers; and explain how architecture and operational choices support marketing, product, and business decisions while balancing tooling investment and team skills.