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 pipelines and streaming processing; orchestration and workflow management; and choices 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 and observability alerting cost and performance trade offs and capacity planning. 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.