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Rippling Backend Engineer Interview Questions

37 practice questions for Rippling Backend Engineer interviews

Rippling backend engineer interviews typically focus on APIs, databases, system design, concurrency, caching, and data structures.

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System Design
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Medium
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Hard
system design Hard Verified Question #1

1. Top 5 Rippling System Design Questions


Category: Sliding window system design problem
These are commonly asked system design questions from Rippling interviews
Input: Given input
Output: Computed result
coding Medium Verified Question #2

2. [AI Enabled Coding] Card Game


Category: String coding problem

Question You are building a simplified card game where each player has a hand of cards and the higher-rated hand wins. Each hand contains exactly...

Input: String
Output: Computed result
coding Hard Verified Question #3

3. [AI Enabled Coding] Design Logger


Category: Array coding problem

Question You need to design a logger library for a new application. The design should be able to allow us to easily add future loggers, like a db...

Input: Array
Output: Printed output
coding Medium Verified Question #4

4. [AI Enabled Coding] Food Delivery Company


Category: String coding problem

Question You are building a driver payment system for a food delivery company. The accounting team needs to track how much money is owed to drivers...

Input: String
Output: Integer
coding Hard Verified Question #5

5. [AI Enabled Coding] Rule Evaluator


Category: String coding problem

Question You need to build a rule evaluation system for a corporate credit card platform. Managers should be able to create rules that enforce...

Input: List
Output: Computed result
technical Medium Verified Question #6

6. How to pass AI Enabled Coding Rounds From FAANG Interviewer


Category: Algorithm coding problem

Tips For AI Coding Rounds AI coding rounds are not as different from regular coding rounds as you might think. The interviewer still needs to get...

Input: Given input
Output: Computed result
system design Senior messaging #1

1. Design a Message Queue for Real-time Notifications in Rippling

Rippling needs a robust message queue to handle real-time notifications for its users, allowing messages to be sent and processed efficiently across different parts of the system.
Class Design: Define a class MessageQueue with methods to enqueue new messages, dequeue messages for processing, and check the status of messages in the queue.
  • enqueue(message: str): Adds a message to the queue.

  • dequeue() -> str: Retrieves and removes the oldest message from the queue.

  • getQueueSize() -> int: Returns the current number of messages in the queue.


Example 1:
Input: enqueue('Welcome to Rippling')
Input: enqueue('New Notification')
Input: dequeue()
Output: 'Welcome to Rippling'
Example 2:
Input: getQueueSize()
Output: 1
Explanation: After dequeueing a message, only one message remains in the queue.
Constraints:
  • 1 <= message.length <= 100

  • At most 10^6 messages can be processed in a year.
system design Senior api design #2

2. Design a Job Scheduler for Rippling's Employee Tasks Management System

Rippling manages numerous tasks for employees, requiring an efficient job scheduler that can prioritize tasks based on dependencies and timings. This scheduler must operate at scale to accommodate all employees seamlessly.
Class Design: Define a class JobScheduler that manages and schedules multiple jobs, ensuring that dependencies are resolved and jobs are executed at the right time.
  • addJob(jobId: int, dependencies: List[int]): Adds a new job with its dependencies.

  • executeJobs(time: int): Executes all jobs that are due at the specified time.

  • getJobStatus(jobId: int) -> str: Returns the current status (pending, executed, failed) of a specified job.


Example 1:
Input: addJob(1, [])
Input: addJob(2, [1])
Input: executeJobs(1)
Output: Job 1 executed successfully.
Example 2:
Input: addJob(3, [2])
Input: executeJobs(1)
Output: Job 2 is still pending due to dependencies.
Constraints:
  • 1 <= jobId <= 1000

  • Each job can have at most 5 dependencies.
coding Hard sliding window #3

3. [OA] Sliding Window — Track active hours for Rippling's employee monitoring system

Rippling aims to keep track of how many employees are active within certain hours to optimize productivity resources. A sliding window approach can facilitate this by keeping real-time data.
Problem Statement: Given an array activeHours, where each integer represents the hours when employees are active, calculate the maximum number of employees that can be active during any specific period of time using the sliding window technique.
Example 1:
Input: activeHours = [1, 2, 2, 3, 3, 4, 5]
Output: 5
Explanation: The peak active hours would merge to add up to 5 employees active between hour 1 and hour 5.
Example 2:
Input: activeHours = [1, 3, 4, 6, 7]
Output: 2
Explanation: The sliding window would find two peak hours.
Constraints:
  • 1 <= activeHours.length <= 10^4

  • 0 <= activeHours[i] <= 24

  • Hours are represented in a 24-hour format.
coding Hard dynamic programming #4

4. [OA] Dynamic Programming — Optimize employee tax calculations in Rippling's payroll system

In Rippling's payroll system, calculating employee taxes accurately and efficiently is vital. As the number of employees grows, optimizing this process using effective algorithms becomes essential.
Problem Statement: Given an array of unique employees where each employee has a specific tax bracket as an integer, find the most optimal way to calculate the total taxes owed using Dynamic Programming. Return the total amount owed after optimizing the tax determination based on the employee array.
Example 1:
Input: employees = [1000, 2500, 4500, 6000]
Output: 1450
Explanation: The tax calculations for employees will yield a total of 1450 after applying the optimal tax structure.
Example 2:
Input: employees = [3000, 4000, 5500]
Output: 850
Explanation: The system optimizes the calculation to achieve a total of 850 based on the tax rules applied.
Constraints:
  • 1 <= employees.length <= 10^4

  • 0 <= employees[i] <= 10^6

  • Every employees value is unique.

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