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This is the kind of question where you can go a hundred different directions and that's sort of the problem.
Start by clarifying the business requirements and key use cases (e.g., transaction reporting, fraud detection, reconciliation) to determine the schema's grain and dimensions. Then propose a dimensional model (star schema) with fact tables for payments and related events, and dimension tables for merchants, customers, payment methods, etc., while discussing trade-offs like normalization vs. denormalization and batch vs. streaming ingestion.
Pro tip: Demonstrate awareness of slowly changing dimensions (SCDs) for entities like merchants and customers, and explain how you'd handle late-arriving data and idempotency in a payments context.
Ask questions to understand the primary analytical needs, data volume, latency requirements, and key metrics (e.g., transaction volume, success rates, revenue).
Determine the central fact tables (e.g., payment transactions, refunds, disputes) and the dimensions (e.g., merchant, customer, payment method, date, geography) that provide context.
Propose a star or snowflake schema, defining keys, relationships, and granularity. Discuss whether to use a normalized or denormalized approach for performance.
Explain partitioning, indexing, and distribution strategies (e.g., by date or merchant) to handle large data volumes and ensure query performance.
Cover trade-offs between consistency and availability, batch vs. streaming, and how the schema can evolve with changing business needs (e.g., SCDs).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.