Reevo Interview Questions (2026)
1 questions · InterviewDB (1)
Top topics
CRM System - Object-Oriented Design for a Customer Relationship Manager
Question Details
Round 1 OOD / Coding
Problem
Design the core of a CRM system. The system tracks Contacts, Companies, and Deals. A Contact belongs to a Company. A Deal has an associated Contact, a value, and a stage (LEAD, QUALIFIED, CLOSED_WON, CLOSED_LOST). Implement the following:
python
class CRMSystem:
def add_company(self, company_id: str, name: str) -> None: ...
def add_contact(self, contact_id: str, name: str, company_id: str) -> None: ...
def create_deal(self, deal_id: str, contact_id: str, value: float) -> None: ...
def advance_stage(self, deal_id: str) -> str: ...
def total_pipeline_value(self, stage: str) -> float: ...
def deals_for_company(self, company_id: str) -> list[dict]: ...
Example
crm.add_company("c1", "Acme")
crm.add_contact("p1", "Alice", "c1")
crm.create_deal("d1", "p1", 10000.0) # stage = LEAD
crm.advance_stage("d1") # -> "QUALIFIED"
crm.total_pipeline_value("QUALIFIED") # -> 10000.0
crm.deals_for_company("c1") # -> [{deal_id, value, stage, contact}]
Follow-ups
- How would you implement deal history so you can see when each stage transition happened?
- A contact can move to a different company — how does that affect existing deals?
- Design an activity log (calls, emails, notes) attached to deals or contacts.
- How would you generate a sales funnel report showing conversion rates between stages?
Topics
Related companies
Reevo Interview Process Overview
The Reevo 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 Reevo runs a calibrated process consistent with industry norms for companies of its tier.
Difficulty calibration: Reevo 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 Reevo Question Reports
Real candidate-reported interview questions are a calibration tool, not a memorization target. Reevo 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 Reevo 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 Reevo'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 Reevo Interview Mistakes
Reports tagged "no hire" at Reevo 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.