Scale Engineer

Software Engineer

Software EngineerP3Medium to Hard

This interview process is for a Software Engineer position at Splunk, specifically at the P3 level. It is designed to assess a candidate's technical skills, problem-solving abilities, and cultural fit within Splunk.

Rounds·3

Timeline·~14d

Experience·4 - 7 yrs

Comp band·US$120000 - US$160000

Interview time·150 min

Evaluation

What they measure.

  • Technical Proficiency: Depth of knowledge in relevant programming languages, data structures, algorithms, and system design.
  • Problem-Solving Skills: Ability to analyze complex problems, devise effective solutions, and articulate thought processes.
  • Communication Skills: Clarity in explaining technical concepts, active listening, and ability to engage in constructive discussions.
  • Collaboration and Teamwork: Experience working effectively in a team environment, sharing knowledge, and contributing to team goals.
  • Cultural Fit: Alignment with Splunk's values, such as innovation, customer focus, and integrity.

Preparation

How to prepare.

Tips

  1. Review fundamental computer science concepts: data structures, algorithms, operating systems, and databases.
  2. Practice coding problems on platforms like LeetCode, HackerRank, or AlgoExpert, focusing on medium to hard difficulty.
  3. Study system design principles and common architectural patterns (e.g., microservices, caching, load balancing).
  4. Prepare to discuss your past projects in detail, highlighting your contributions, challenges, and learnings.
  5. Research Splunk's products, mission, and values to understand how your skills align with the company's goals.
  6. Practice behavioral questions using the STAR method (Situation, Task, Action, Result).
  7. Prepare thoughtful questions to ask the interviewers about the role, team, and company culture.

Study plan

Fig · Study plan — 04 phases

01 / 04
01

Phase 01 of 04

Data Structures and Algorithms

Weeks 1-2: Data Structures & Algorithms (Arrays, Trees, Graphs, DP). Practice implementation and complexity analysis.

Weeks 1-2: Focus on Data Structures and Algorithms. Cover arrays, linked lists, trees, graphs, hash tables, sorting, searching, dynamic programming, and greedy algorithms. Practice implementing these and analyzing their time and space complexity.

Questions

Commonly asked.

  • Describe a complex system you designed or contributed to. What were the key challenges and how did you address them?
  • How would you design a system to handle real-time log aggregation and analysis for a large enterprise?
  • Given a scenario where a web application is experiencing high latency, how would you diagnose and resolve the issue?
  • Explain the difference between concurrency and parallelism and provide examples of when each is appropriate.
  • Tell me about a time you disagreed with a technical decision made by your team. How did you handle it?
  • How do you stay updated with the latest technologies and trends in software engineering?
  • Write a function to find the k-th largest element in an unsorted array.
  • Design a URL shortening service like bit.ly.
  • What are the trade-offs of using a monolithic architecture versus a microservices architecture?
  • How would you ensure the security of a distributed system?

Locations

Regional differences.

Fig · Regions — 03 locations

01 / 03

Location

USA

Interview focus

System Design: Emphasis on scalability, reliability, and performance of distributed systems.Problem Solving: Ability to break down complex problems and devise efficient solutions.Coding Proficiency: Clean, efficient, and well-tested code.Collaboration: Teamwork and communication skills.

Common questions

  • How would you design a distributed caching system for a high-traffic web application?
  • Describe a challenging bug you encountered and how you debugged it.
  • Explain the trade-offs between different database technologies (e.g., SQL vs. NoSQL).
  • How do you ensure code quality and maintainability in a large codebase?
  • Tell me about a time you had to work with a difficult stakeholder.

Tips

  • For US-based interviews, be prepared for in-depth discussions on distributed systems and cloud technologies.
  • For European locations, expect a strong focus on data structures, algorithms, and practical coding challenges.
  • For APAC regions, emphasize your experience with large-scale data processing and performance optimization.

Rounds

Round-by-round.

Expand a step for evaluation criteria, sample questions, and prep notes.

Med

Technical Interview - Data Structures And Algorithms · 45 min · Software Engineer

This round focuses on your core computer science knowledge. You will be asked to solve coding problems, typically involving data structures and algorithms. The interviewer will assess your ability to write clean, efficient, and correct code, as well as your problem-solving approach and communication skills.

What they look for

Strong grasp of fundamental data structures and algorithms.Ability to translate a problem into efficient code.Clear communication of approach and trade-offs.Attention to detail and handling of edge cases.

Evaluation

  • Correctness of the solution.
  • Efficiency of the algorithm (time and space complexity).
  • Code quality, readability, and maintainability.
  • Ability to communicate the solution and thought process.

Sample questions

  • Given a binary tree, find its inorder traversal.

    Data StructuresTreesRecursion

  • Implement a function to reverse a linked list.

    Data StructuresLinked ListsPointers

  • Find the first non-repeating character in a string.

    StringsHash MapsTime Complexity

  • Given two sorted arrays, merge them into a single sorted array.

    ArraysSortingTwo Pointers

Prep for this round

  1. 01Practice coding problems on platforms like LeetCode, focusing on common patterns.
  2. 02Understand the time and space complexity of your solutions.
  3. 03Be prepared to explain your thought process step-by-step.
  4. 04Practice writing code on a whiteboard or in a shared editor.

Common rejection reasons

  • ×Inability to articulate thought process clearly.
  • ×Lack of fundamental understanding of data structures and algorithms.
  • ×Poor coding practices (e.g., unreadable code, no error handling).
  • ×Failure to consider edge cases or constraints.

DSA

Coding questions at Splunk.

Frequently reported on Splunk loops

View all at Splunk

Other guides

More at Splunk.