The scope felt manageable at first but I spent too long on the loader abstraction and not enough time thinking through what 'canonical' actually means when two systems disagree on field names or types.
Start by clarifying the requirements and assumptions, then propose a modular pipeline architecture with separate loaders, validators, and a canonical model. Walk through the design step-by-step, emphasizing data modeling choices, validation strategies, and trade-offs for scalability and maintainability.
Pro tip: Demonstrate awareness of real-world data issues like schema evolution and partial failures, and suggest idempotent processing to ensure reliability. Highlight how your design supports extensibility for future data sources without major refactoring.
Ask about data volume, velocity, source formats, and downstream comparison needs. State assumptions about batch vs. streaming, schema stability, and error handling expectations.
Outline separate loaders for each source, a validation layer, and a canonical model. Explain how these components interact and can be extended or replaced independently.
Propose a shared in-memory representation (e.g., a normalized record class) that captures common fields and types. Discuss mapping from source-specific schemas to the canonical model.
Describe validation rules for fields and types, and how to handle invalid records (e.g., logging, dead-letter queue). Emphasize fail-fast vs. collect-all strategies.
Discuss performance considerations (e.g., streaming vs. batch, memory footprint) and trade-offs between strict validation and flexibility. Mention idempotency and retry mechanisms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.