This one circulates a lot so I'd seen it before, but knowing it's coming and actually executing under time pressure are two different things.
Start by clarifying requirements: data sources, matching criteria, volume, and discrepancy types. Then propose a scalable architecture using hash-based matching or sorted merge, and discuss trade-offs around accuracy, performance, and fault tolerance. Finally, outline a reconciliation algorithm and how to handle edge cases like duplicates and missing transactions.
Pro tip: Emphasize idempotency and auditability: reconciliation systems must be re-runnable without side effects, and every discrepancy should be traceable to its source. Mentioning these shows production maturity beyond just algorithms.
Ask about data sources (e.g., internal DB vs. external API), matching keys (transaction ID, amount+date), data volume, latency needs, and what constitutes a discrepancy (missing, extra, mismatched amount).
Define a canonical transaction record and choose a matching approach: exact key match (hash map) for speed, or fuzzy matching (e.g., amount within tolerance) if keys are unreliable. Consider sorting both sources for merge-based matching.
Describe step-by-step: load and normalize data, index by key, iterate to find matches, flag discrepancies (missing in A, missing in B, amount mismatch), and handle duplicates by grouping or using composite keys.
Discuss partitioning (e.g., by date or merchant), batch processing, and idempotent re-runs. Mention using a persistent store for results and alerting on discrepancies.
Compare in-memory vs. distributed processing, exact vs. fuzzy matching, and real-time vs. batch reconciliation. Cover edge cases like timezone differences, currency conversion, and partial refunds.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.