Gap, the global retail company behind brands like Gap, Old Navy, Banana Republic, and Athleta, hires engineers who can build scalable systems that support millions of customers and complex supply chains. As part of the hiring process, candidates for technical roles are often evaluated through coding interviews that focus on core data structures and algorithms (DSA) concepts.

Gap interview questions typically assess your ability to think logically, write clean code, and solve real-world problems efficiently. While the company may not emphasize extremely advanced algorithmic challenges, candidates are expected to demonstrate strong fundamentals in arrays, strings, hashing, and basic algorithmic problem solving. Practicing targeted questions can help you understand the style and difficulty level of problems commonly asked in Gap technical interviews.

On this page, you’ll find carefully selected DSA problems that reflect the type of coding questions candidates may encounter during the Gap software engineering interview process. Working through these problems will help you strengthen your fundamentals and approach interviews with confidence.

Preparing for a Gap coding interview requires strong fundamentals in data structures, clear communication, and the ability to translate real-world scenarios into efficient code. Gap’s engineering teams typically look for candidates who can solve practical problems while writing readable and maintainable solutions.

Many interview questions focus on fundamental algorithmic thinking rather than extremely complex puzzles. Interviewers often evaluate how well you approach a problem, explain your reasoning, and optimize your solution step by step.

What to expect in Gap technical interviews:

• Core DSA problems: Questions often involve arrays, strings, hash maps, and simple recursion or iteration patterns.
• Problem explanation: You may be asked to walk through your approach before writing code and discuss edge cases.
• Code clarity: Clean, readable code is important since Gap values maintainable engineering practices.
• Optimization discussion: After solving the problem, interviewers may ask how you would improve time or space complexity.

Preparation strategy:

• Practice fundamental DSA problems and focus on writing bug‑free code quickly.
• Explain your thought process clearly while solving problems.
• Review common patterns such as sliding window, hash map counting, and two-pointer techniques.
• Practice solving problems on a whiteboard or shared editor to simulate interview conditions.

Even though Gap interviews may not always involve extremely difficult algorithmic challenges, demonstrating structured thinking, strong fundamentals, and good communication can significantly improve your chances of success.