Scale Engineer

Software Engineer

Software EngineerTech Lead Software EngineerHigh

This interview process is designed to assess candidates for the Tech Lead Software Engineer position at Nike. It evaluates technical expertise, leadership potential, problem-solving abilities, and cultural fit within the Nike environment.

Rounds·3

Timeline·~14d

Experience·7 - 10 yrs

Comp band·US$140000 - US$180000

Interview time·150 min

Evaluation

What they measure.

  • Technical proficiency in relevant programming languages and frameworks.
  • Ability to design scalable and maintainable software systems.
  • Problem-solving skills and analytical thinking.
  • Leadership qualities and ability to mentor engineers.
  • Communication and interpersonal skills.
  • Understanding of software development best practices (e.g., Agile, CI/CD, testing).

Preparation

How to prepare.

Tips

  1. Thoroughly review Nike's mission, values, and recent product launches.
  2. Brush up on core computer science concepts, data structures, and algorithms.
  3. Practice system design problems, focusing on scalability, reliability, and performance.
  4. Prepare examples of your leadership experience, technical challenges overcome, and successful project deliveries using the STAR method (Situation, Task, Action, Result).
  5. Understand common software development methodologies and best practices.
  6. Research current trends in software engineering and cloud technologies relevant to Nike's business.
  7. Be ready to discuss your career aspirations and how they align with a Tech Lead role at Nike.

Study plan

Fig · Study plan — 04 phases

01 / 04
01

Phase 01 of 04

Data Structures and Algorithms

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

Weeks 1-2: Focus on core data structures (arrays, linked lists, trees, graphs, hash tables) and algorithms (sorting, searching, dynamic programming, graph traversal). Practice implementing these in your primary programming language. Review Big O notation for time and space complexity analysis.

Questions

Commonly asked.

  • Tell me about a time you led a team through a challenging technical project.
  • How do you ensure the quality and scalability of the software your team produces?
  • Describe a situation where you had to make a difficult technical trade-off. What was your reasoning?
  • How do you mentor and develop junior engineers?
  • What is your experience with cloud platforms like AWS, Azure, or GCP?
  • How do you handle disagreements within your team regarding technical direction?
  • Design a system for [e.g., a ride-sharing service, a social media feed, an e-commerce recommendation engine].
  • What are the key principles of good API design?
  • How do you approach performance optimization in a distributed system?
  • Tell me about a time you failed. What did you learn from it?

Locations

Regional differences.

Fig · Regions — 02 locations

01 / 02

Location

Seattle

Interview focus

Leadership and mentorshipTechnical decision-makingCross-functional collaborationCloud architecture (especially in Seattle)Agile methodologies

Common questions

  • How would you handle a situation where a junior engineer on your team is struggling with a complex task?
  • Describe a time you had to influence a team to adopt a new technology or process.
  • What are your strategies for ensuring code quality and maintainability in a large project?
  • How do you balance delivering features quickly with maintaining technical debt?
  • In our Seattle office, there's a strong emphasis on cloud-native architectures. Be prepared to discuss your experience with AWS/Azure/GCP services relevant to microservices and serverless computing.

Tips

  • Highlight instances where you've mentored junior engineers.
  • Provide specific examples of technical leadership and influence.
  • Be ready to discuss your experience with CI/CD pipelines and automated testing.
  • For Seattle candidates, emphasize your understanding of scalable cloud solutions.

Rounds

Round-by-round.

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

DSA

Coding questions at Nike.

Frequently reported on Nike loops

View all at Nike

Other guides

More at Nike.