Scale Engineer

Software Engineer

Software EngineerL4Hard

The Software Engineer L4 interview at Optiver is designed to assess a candidate's technical proficiency, problem-solving abilities, and cultural fit within the company. This role typically requires a solid understanding of computer science fundamentals, data structures, algorithms, and experience in software development. The interview process is rigorous and aims to identify individuals who can contribute to Optiver's innovative trading environment.

Rounds·3

Timeline·~14d

Experience·2 - 5 yrs

Comp band·US$110000 - US$150000

Interview time·135 min

Evaluation

What they measure.

  • Problem-solving approach and analytical skills.
  • Knowledge of data structures and algorithms.
  • Coding proficiency and best practices.
  • System design capabilities.
  • Communication skills.
  • Cultural fit and teamwork.

Preparation

How to prepare.

Tips

  1. Review fundamental computer science concepts, including data structures (arrays, linked lists, trees, graphs, hash tables) and algorithms (sorting, searching, dynamic programming, graph traversal).
  2. Practice coding problems on platforms like LeetCode, HackerRank, or Coderbyte, focusing on medium to hard difficulty.
  3. Study system design principles, including scalability, reliability, availability, and performance.
  4. Understand common design patterns and their applications.
  5. Prepare for behavioral questions by reflecting on past experiences using the STAR method (Situation, Task, Action, Result).
  6. Research Optiver's business, culture, and technology stack.
  7. Practice explaining your thought process clearly and articulating your solutions.
  8. Familiarize yourself with low-latency programming concepts and optimization techniques if applicable to the role.
  9. Prepare questions to ask the interviewer about the role, team, and company.

Study plan

Fig · Study plan — 05 phases

01 / 05
01

Phase 01 of 05

Data Structures and Algorithms

Weeks 1-2: Data Structures & Algorithms fundamentals. Practice implementations and complexity analysis.

Weeks 1-2: Focus on core data structures and algorithms. Practice implementing and analyzing the time and space complexity of common algorithms. Cover topics like arrays, linked lists, stacks, queues, trees (binary trees, BSTs, heaps), graphs, sorting algorithms (quicksort, mergesort), searching algorithms (binary search), and basic dynamic programming.

Questions

Commonly asked.

  • Given an array of integers, find the contiguous subarray with the largest sum.
  • Design a system to store and retrieve user profiles efficiently.
  • Explain the difference between a process and a thread.
  • How would you optimize a slow database query?
  • Describe a time you had to deal with a difficult stakeholder.
  • Implement a function to reverse a linked list.
  • What are the trade-offs between using a relational database and a NoSQL database?
  • How do you approach debugging a complex software issue?
  • Tell me about a project you are particularly proud of.
  • What is the time complexity of quicksort?
  • How would you design a rate limiter?
  • Describe your experience with version control systems like Git.
  • What are the principles of object-oriented programming?
  • How do you handle conflicting priorities?
  • Write a function to find the kth smallest element in an unsorted array.

Locations

Regional differences.

Fig · Regions — 03 locations

01 / 03

Location

Amsterdam

Interview focus

Emphasis on practical application of algorithms and data structures in financial contexts.Deep dive into system design for performance and reliability.Understanding of concurrency and multi-threading for trading systems.Familiarity with C++ or Java performance tuning.Behavioral questions focused on collaboration and handling pressure.

Common questions

  • Discuss a challenging technical problem you solved in a previous role.
  • How do you approach designing a scalable system for real-time data processing?
  • Explain the trade-offs between different database technologies for high-frequency trading data.
  • Describe your experience with low-latency programming and optimization techniques.
  • How do you ensure code quality and maintainability in a fast-paced environment?

Tips

  • Be prepared to discuss specific examples of optimizing code for speed.
  • Understand the nuances of memory management in performance-critical applications.
  • Research Optiver's technology stack and trading strategies.
  • Practice explaining complex technical concepts clearly and concisely.
  • Highlight any experience with distributed systems or high-frequency trading.

Rounds

Round-by-round.

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

DSA

Coding questions at Optiver.

Frequently reported on Optiver loops

View all at Optiver

Other guides

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