Ramp, a fast-growing fintech company revolutionizing business spending, is renowned for its rigorous technical hiring process. Ramp OA serves as the critical first hurdle for engineering candidates, designed to evaluate the ability to solve real-world problems efficiently. This guide breaks down Ramp OA structure, provides sample questions, and offers strategic preparation tips to help you succeed in this competitive stage.
Understanding Ramp OA
Ramp’s OA typically lasts 60–90 minutes and includes 2–3 coding problems focused on algorithms, data structures, and problem-solving in fintech contexts. The assessment is often hosted on platforms like HackerRank or CodeSignal, and questions reflect Ramp’s core domains: payment processing, transaction reconciliation, and financial data management.
Key Focus Areas
- Algorithmic Efficiency: Solutions must handle large datasets within acceptable time limits.
- Financial Scenarios: Questions may involve transaction categorization, fraud detection, or expense tracking.
- Code Clarity: Readability and maintainability are prioritized, even under time pressure.
Ramp OA Questions & Solutions
1. Payment Routing Optimization
Problem Statement
Ramp needs to route transactions to the payment gateway with the lowest cost. Each gateway has a different fee structure (fixed fee + percentage fee) and may have restrictions on transaction amount ranges. Design an algorithm to select the optimal gateway for each transaction.
Input
{
"gateways": [
{"name": "Stripe", "fixed_fee": 0.30, "percent_fee": 0.029, "min_amount": 0, "max_amount": 5000},
{"name": "PayPal", "fixed_fee": 0.40, "percent_fee": 0.025, "min_amount": 0, "max_amount": 10000},
{"name": "Square", "fixed_fee": 0.15, "percent_fee": 0.035, "min_amount": 0, "max_amount": 2000}
],
"transactions": [100, 500, 2000, 15000]
}
Output
[
{"transaction": 100, "gateway": "Square", "total_fee": 3.65},
{"transaction": 500, "gateway": "Stripe", "total_fee": 15.80},
{"transaction": 2000, "gateway": "PayPal", "total_fee": 50.40},
{"transaction": 15000, "gateway": "PayPal", "total_fee": 379.40}
]
Note: Example output includes a transaction (15000) exceeding the stated max_amount constraints; a real implementation should enforce those bounds.
2. Transaction Deduplication and Merging
Problem Statement
Ramp needs to identify and merge duplicate user transactions (e.g., multiple authorizations or refunds for the same expense). Design an algorithm to merge duplicate items based on transaction attributes.
Input
[
{"id": "t1", "user_id": "u1", "amount": 100.00, "merchant": "Amazon", "timestamp": "2025-01-15T10:00:00Z"},
{"id": "t2", "user_id": "u1", "amount": 100.00, "merchant": "Amazon", "timestamp": "2025-01-15T10:05:00Z"},
{"id": "t3", "user_id": "u1", "amount": -100.00, "merchant": "Amazon", "timestamp": "2025-01-16T11:30:00Z"},
{"id": "t4", "user_id": "u2", "amount": 50.00, "merchant": "Starbucks", "timestamp": "2025-01-15T14:15:00Z"}
]
Output
[
{"id": "t1", "user_id": "u1", "amount": 100.00, "merchant": "Amazon", "timestamp": "2025-01-15T10:00:00Z", "status": "merged_into(t3)"},
{"id": "t2", "user_id": "u1", "amount": 100.00, "merchant": "Amazon", "timestamp": "2025-01-15T10:05:00Z", "status": "merged_into(t3)"},
{"id": "t3", "user_id": "u1", "amount": 0.00, "merchant": "Amazon", "timestamp": "2025-01-16T11:30:00Z", "status": "resolved"},
{"id": "t4", "user_id": "u2", "amount": 50.00, "merchant": "Starbucks", "timestamp": "2025-01-15T14:15:00Z", "status": "normal"}
]
3. Real-time Expense Categorization
Problem Statement
Ramp needs to automatically categorize transactions into predefined categories (e.g., “Travel,” “Office,” “Food”). Design a system to classify transactions based on their descriptions and merchant information.
Input
"rules": [
{"category": "Travel", "keywords": ["airlines", "hotel", "flight", "uber"], "merchants": ["Delta", "Hilton"]},
{"category": "Office", "keywords": ["stationery", "printer", "software"], "merchants": ["Staples", "Microsoft"]},
{"category": "Food", "keywords": ["restaurant", "cafe", "bar"], "merchants": ["Starbucks", "McDonald's"]}
],
"transactions": [
{"id": "tx1", "description": "Delta Airlines Flight to NY", "merchant": "Delta"},
{"id": "tx2", "description": "Starbucks Coffee", "merchant": "Starbucks"},
{"id": "tx3", "description": "Buy printer paper", "merchant": "Office Depot"}
]
Output
[
{"id": "tx1", "category": "Travel"},
{"id": "tx2", "category": "Food"},
{"id": "tx3", "category": "Office"}
]
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