Interview Experience - 154 - Rippling | Software Development Engineer
Summary
Job Role: Software Development Engineer
Number of Rounds: 5
Offer Status: Rejected
Location: Bangalore
Candidate Name: Not disclosing due to signed NDA
Interview Process
The interview process at Rippling consisted of five rounds covering a mix of Low Level Design (LLD), High Level Design (HLD), and Data Structures & Algorithms (DS/Algo).
The rounds were designed to test:
Design skills — both at a component level (LLD) and at a system level (HLD).
Problem solving ability — especially in algorithmic thinking and optimizations.
Handling edge cases and scalability — thinking beyond basic functionality.
Each round had a clear objective and tested different dimensions of technical expertise.
Preparation Guide
While there wasn’t a specific preparation source mentioned, for this type of interview I would recommend:
For LLD: Practice real-world object-oriented design problems, focusing on flexibility and handling edge cases.
For HLD: Go deep into system design concepts like batching, idempotency, and rollup aggregations.
For DS/Algo: Consistent practice on platforms like LeetCode, focusing on medium-to-hard level problems, particularly around strings, grids, and prefix sums.
General: Keep brushing up on data manipulation and aggregation concepts, as they often come up in unexpected ways.
Interview Rounds
Round 1: Screening (LLD)
Duration: ~45 minutes
Difficulty Level: Medium
Experience:
This was a screening round focused on Low Level Design, centered on data manipulation of employee records.
The problem statement was:
Given a list of employee details, group them by department and display the total salary for each department.
Extend this functionality to allow grouping by any arbitrary key and applying any aggregate function (for example, minimum salary or average salary).
Implement a
filterByfunctionality that could be applied to the employee dataset.
The interviewer was looking for clean code, good use of collections, and flexibility in design so that new grouping/aggregation logic could be easily added. Discussions also touched upon the trade-offs between hardcoding logic versus designing extensible components.
Key Learnings:
Refresh fundamental aggregation concepts and think about generic implementations instead of fixed ones.
Understand database-style operations like
GROUP BY,MIN,MAX, andAVGfrom a coding perspective.Keep your design modular to support future extensions without rewriting the entire logic.
Round 2: LLD + Simple DS/Algo
Duration: ~60 minutes
Difficulty Level: Medium
Experience:
This round involved designing a 2D canvas with the ability to draw() and move() rectangles. The questions were:
How would you manage a rectangle on a grid when it’s moved outside the boundaries?
How would you adapt your design to handle an infinite grid?
The focus here was on handling edge cases, managing the state of objects on the grid, and thinking about the scalability of the design when transitioning from a bounded grid to an unbounded one. The interviewer probed deeper into how data structures could efficiently store grid data when the grid is infinite, hinting at sparse representations.
Key Learnings:
Practice grid-based problems, especially those involving movement and spatial constraints.
For infinite grids, think about using hash maps or coordinate-based storage instead of large arrays.
Always clarify constraints before jumping into implementation.
Round 3: High Level Design (HLD)
Duration: ~75 minutes
Difficulty Level: Difficult
Experience:
The problem was to design a Rollup-Aggregation system for events.
Given:
Client SDKs that send events to a server.
Server that listens for these events.
Requirements:
Optimize events sent by batching them.
Handle failing requests using idempotency.
On the server, implement rollup aggregation to store metrics at intervals of 15 minutes, 1 hour, and 1 day.
This discussion touched on event streaming, message queues, and data storage strategies. The interviewer expected details on how the batching logic would work at the client-side, how to guarantee idempotency to avoid duplicate data, and how rollup aggregation would be implemented efficiently without overloading the database.
Key Learnings:
Study system design for event-driven architectures, especially in the context of analytics.
Understand batching mechanisms, trade-offs between batch size and latency.
Learn about idempotency keys and their implementation to ensure consistent results in case of retries.
Rollup aggregations are common in analytics systems — practice designing them for time-series data.
Round 4: DS/Algo (Strings)
Duration: ~45 minutes
Difficulty Level: Hard
Experience:
The problem was based on counting duplicate substrings in a given string and finding the longest duplicate substring. The interviewer referenced the LeetCode problem.
This was a high-complexity string manipulation problem requiring efficient algorithms like suffix arrays or binary search combined with hashing. I struggled with this round, mainly because I could not arrive at an optimal solution within the given time.
Key Learnings:
Practice advanced string algorithms, especially suffix arrays, suffix trees, and rolling hash.
Focus on optimizing brute force solutions into O(n log n) or better.
For hard problems, keep calm and explain your thought process — even partial progress is valuable.
Round 5: DS/Algo (2D Prefix Sum)
Duration: ~45 minutes
Difficulty Level: Medium
Experience:
The problem was similar to Matrix Block Sum. I used the 2D prefix sum technique to solve it. The solution was accepted, and the interviewer appreciated the optimized approach.
Key Learnings:
2D prefix sums are powerful for reducing repeated calculations in matrix-based problems.
Always verify your indexing carefully to avoid off-by-one errors in prefix sum arrays.
Final Thoughts
The Rippling interview process was well-structured and tested a good range of skills — from component-level design to system design and algorithmic problem solving.
Key takeaways for future candidates:
Prepare for both LLD and HLD. Many companies, including Rippling, assess your ability to design at multiple levels.
Do not underestimate grid-based problems — both bounded and unbounded scenarios are common.
For system design, be comfortable discussing optimizations like batching, idempotency, and aggregation strategies.
Keep practicing DS/Algo regularly, especially on advanced topics like string algorithms and 2D array techniques.
Even if you miss one or two rounds, keep your composure for the remaining ones — performance in later rounds still matters.