Plaid Interview Questions (May 2026)
3 questions · 3 experiences · InterviewDB (6)
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Coupon System - Apply and Stack Discount Coupons on an Order
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Coupon System - Apply and Stack Discount Coupons on an Order
Question Details
Problem
Design a coupon application system for an e-commerce checkout. Coupons have types: percent_off, fixed_off, buy_x_get_y_free, and category_discount. Rules:
- At most 2 coupons can be applied per order.
- Coupons cannot reduce the price below $0.
- Some coupons are exclusive (cannot stack with others).
Implement:
- apply_coupons(order, coupon_codes) -> CheckoutResult
python
class CouponSystem:
def apply_coupons(
self,
order: dict, # {"items": [{"id", "category", "price", "qty"}]}
coupon_codes: List[str]
) -> dict: # {"original": float, "discount": float, "final": float, "applied": List[str]}
...
Example:
order = {"items": [{"id":"i1","category":"electronics","price":200,"qty":1}]}
coupons = ["SAVE10PCT", "TECH20"]
# SAVE10PCT: 10% off entire order
# TECH20: $20 off electronics category
apply_coupons(order, coupons) ->
{"original": 200, "discount": 40.0, "final": 160.0, "applied": ["SAVE10PCT","TECH20"]}
Follow-ups
- How do you determine the optimal order to apply coupons to maximize the discount?
- How do you enforce the exclusivity constraint when 3+ coupons are submitted?
- How would you validate coupon expiry and single-use restrictions?
- How do you handle a
buy_x_get_y_freecoupon across multiple cart items?
Topics
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Plaid Interview Process Overview
The Plaid interview process typically includes a recruiter screen, one to two technical phone screens, and a 4-6 round on-site or virtual on-site loop. Each round serves a distinct calibration purpose: coding rounds measure correctness, code quality, and complexity reasoning; system design rounds measure architectural judgment at the appropriate level; behavioral rounds measure ownership, leadership scope, and collaboration. Reports tagged on LeakCode from 2024-2026 show Plaid runs a calibrated process consistent with industry norms for companies of its tier.
Difficulty calibration: Plaid coding rounds typically run medium difficulty with follow-up depth as the senior discriminator. System design rounds expect production-grade trade-off articulation at L4+ levels. Behavioral rounds expect quantified outcomes ("reduced p99 latency from 800ms to 120ms") rather than vague impact claims. The candidates who advance consistently demonstrate clear thinking out loud rather than perfect final answers.
How To Use Plaid Question Reports
Real candidate-reported interview questions are a calibration tool, not a memorization target. Plaid updates its question pool every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage approach: identify the patterns that appear repeatedly in Plaid reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions above by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Plaid's pool. Reports tagged with quantified difficulty and explicit round type are higher-signal than reports without those tags. The metadata filters help you build a focused study plan in 1-2 hours rather than 8-10 hours of unstructured browsing.
Common Plaid Interview Mistakes
Reports tagged "no hire" at Plaid consistently surface a few patterns: jumping into code without clarifying requirements, coding silently for extended periods, missing edge cases (empty input, single element, large input, overflow), producing working code the candidate cannot refactor when probed, and behavioral stories that use "we" instead of "I" diluting individual signal. Strong candidates explicitly avoid these patterns by following a consistent round template.
The single most predictive failure mode in recent reports: not asking clarifying questions. Interviewers are explicitly trained to weight this dimension. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into implementation immediately. Strong candidates also verbalize their approach before writing code; weak candidates code in silence and lose the communication dimension of the round's calibration.