Ramp·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Ramp software engineering interview with a meaty coding problem around transaction data. The whole thing was basically one big design-plus-implementation question about detecting recurring billing patterns, which sounds straightforward until you actually sit down to define what 'recurring' even means.

Questions Asked (1)

Given a list of credit/debit card transactions (each with a date, merchant, amount, and currency), write a solution that identifies recurring transactions and outputs a summary like 'Netflix, $30.00 per month'. Your solution should handle multiple recurrence periods (daily, weekly, monthly), treat each merchant-currency pair separately, and you need to define and justify what counts as 'recurring' in the first place.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The definition part is what actually took time.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the definition of 'recurring' with the interviewer, then propose a solution that groups transactions by merchant and currency, detects patterns based on consistent intervals and amounts, and outputs a summary. Discuss trade-offs between simple heuristics and more robust statistical methods, and consider scalability for large datasets.

Pro tip: Demonstrate product thinking by discussing how false positives (e.g., two coffee purchases) could annoy users, and propose a confidence score or user feedback loop to refine detection.

1. Clarify Requirements and Define 'Recurring'

Ask clarifying questions about data volume, expected recurrence types, and tolerance for false positives/negatives. Propose a working definition: a series of transactions from the same merchant and currency with similar amounts occurring at regular intervals (e.g., within a tolerance window).

2. Design Data Structures and Grouping Strategy

Group transactions by a composite key of merchant and currency. For each group, sort transactions by date and analyze intervals between consecutive transactions to identify potential recurrence patterns.

3. Detect Recurrence Patterns

For each group, compute the differences between consecutive transaction dates and amounts. Use clustering or threshold-based matching to identify consistent intervals (daily, weekly, monthly) and amounts (within a percentage tolerance). Consider minimum number of occurrences to confirm recurrence.

4. Handle Edge Cases and Multiple Periods

Account for irregular intervals due to weekends/holidays (e.g., monthly subscriptions may shift by a few days). Allow detection of multiple recurrence periods per merchant-currency pair if applicable (e.g., a merchant with both weekly and monthly charges).

5. Output Summary and Discuss Trade-offs

Format the output as required (e.g., 'Netflix, $30.00 per month'). Discuss trade-offs: simplicity vs. accuracy, computational complexity (O(n log n) due to sorting), and potential scalability improvements (e.g., streaming or windowed processing).

Key Points to Mention

Definition of 'recurring': consistent interval and amount within tolerance, minimum occurrences (e.g., at least 3).
Grouping by merchant and currency to avoid mixing different currencies.
Handling irregular intervals (e.g., monthly subscriptions on different days) with tolerance windows.
Algorithm complexity: sorting per group leads to O(n log n) overall; can optimize with hash maps and incremental processing.
Trade-offs: false positives vs. false negatives; user experience implications.
Scalability: how to handle large datasets (e.g., distributed processing, sampling, or incremental updates).

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