Misread the whole thing early on and never recovered.
Start by clarifying the scope and requirements of the reconciliation system, such as transaction volume, data sources, and matching criteria. Then outline a high-level architecture that includes data ingestion, matching logic, and discrepancy handling, and dive into key components like idempotency and error recovery. Finally, discuss trade-offs and how you would ensure correctness and scalability.
Pro tip: Emphasize idempotency and exactly-once processing, as these are critical in payment systems to avoid double-counting or missing transactions. Also, mention how you would handle partial failures and retries gracefully.
Ask questions to understand the volume of transactions, data sources (e.g., internal DB, external APIs), matching rules (e.g., by transaction ID, amount, timestamp), and latency requirements. This ensures you design a system that meets actual needs.
Propose how to ingest transaction records from various sources, possibly using a message queue or batch processing. Choose a storage solution that supports efficient querying and is scalable, such as a relational database or a distributed ledger.
Describe the matching logic: exact matches, fuzzy matches for discrepancies, and handling of unmatched records. Consider using a rules engine or a configurable matching service to adapt to different scenarios.
Outline how to detect and resolve mismatches, such as missing transactions, duplicates, or amount differences. Include alerting, manual review queues, and automated retries with idempotency keys.
Discuss partitioning, sharding, and asynchronous processing to handle high volume. Mention monitoring, logging, and metrics to track reconciliation success rates and system health.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.