Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Meta SWE interview with a banking system extension problem. Pretty focused on getting the implementation right under pressure, and the ranking logic had some gotchas worth knowing about.

Questions Asked (1)

You have an existing banking system. Add a top_spenders(timestamp, n) function that returns the top n accounts ranked by total outgoing transfer amount up to that timestamp, formatted as 'account_id(total_outgoing)'. Deposits don't count, only transfers where the account is the source. Ties go alphabetically by account_id ascending. If fewer than n accounts exist, return all of them.

Algorithms & Data StructuresSystem Design

Author's notes

The deposit exclusion is the part that trips people up.

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AI HintsAI Generated

Suggested Approach

Clarify requirements and constraints, then propose a data model that supports efficient aggregation of outgoing transfers per account up to a given timestamp. Discuss indexing and query strategies, and outline how to handle ties and the top-n selection. Finally, analyze time/space complexity and potential optimizations for scale.

Pro tip: Emphasize that this is a read-heavy analytical query on transactional data; propose a materialized view or pre-aggregated summary table updated incrementally to avoid scanning the entire transfer history on each call.

1. Clarify Requirements and Constraints

Ask about data volume, query frequency, latency requirements, and whether timestamps are monotonic. Confirm that only outgoing transfers count and that ties are broken by account_id ascending.

2. Design Data Model and Indexing

Propose a transfers table with source_account_id, amount, and timestamp. Suggest an index on (timestamp, source_account_id) or a composite index to efficiently filter by time and group by account.

3. Outline Query Strategy

Describe a SQL query that filters transfers up to the timestamp, groups by source_account_id, sums amounts, orders by sum descending and account_id ascending, and limits to n. Mention using window functions or subqueries if needed.

4. Address Scalability and Performance

Discuss pre-aggregation (e.g., daily/hourly rollups) or materialized views to speed up repeated queries. Consider partitioning by time and using caching for frequent timestamps.

5. Analyze Complexity and Trade-offs

State time complexity of the naive approach (O(T log T) where T is number of transfers) and how pre-aggregation reduces it. Discuss trade-offs between storage, freshness, and query latency.

Key Points to Mention

Only outgoing transfers (source account) are summed; deposits and incoming transfers are excluded.
Tie-breaking: when total amounts are equal, sort by account_id ascending.
Use of SQL aggregation with GROUP BY and ORDER BY, and LIMIT n.
Indexing on timestamp and source_account_id to optimize filtering and grouping.
Pre-aggregation or materialized views for performance at scale.
Handling edge cases: fewer than n accounts, no transfers, timestamp before any transfers.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.