VIEWS Software Engineer Interview: Questions & Prep (2026)

VIEWS currently has 40 open Software Engineer roles as of July 2026, making it one of the more active hiring companies right now. Candidates report a process that typically runs 3-5 rounds, covering data structures and algorithms, system design, and a behavioural or HR discussion. The process is largely conducted over video calls, with some candidates reporting an online coding assessment in the first round. Preparation across all three pillars (coding, design, behaviour) is important to clear each stage.

Based on what candidates report from VIEWS Software Engineer interviews, these questions come up frequently:

1. Walk me through a challenging technical problem you solved recently and how you approached debugging it.
2. How would you design a scalable notification system that handles millions of users?
3. Explain the difference between a process and a thread. When would you use one over the other?
4. Given an array of integers, find two numbers that add up to a target sum. What is the time and space complexity of your solution?
5. How do you ensure code quality in a team setting? What practices do you follow?
6. Describe a situation where you had to meet a tight deadline. How did you prioritise your tasks?
7. What is your experience with microservices architecture? What are the trade-offs compared to a monolith?
8. How would you handle a production outage at 2 AM? Walk me through your incident response process.
9. Tell me about a time you disagreed with a technical decision made by your team. What did you do?
10. How do you approach writing unit tests for a feature you just built?
11. What design patterns have you used in production, and why did you choose them?
12. Where do you see software engineering headed in the next few years, and how are you preparing for it?

Q: Walk me through a challenging technical problem you solved recently.

*Situation:* Our e-commerce platform was experiencing random checkout failures during peak traffic, affecting roughly one in ten users at sale events.

*Task:* I was responsible for identifying the root cause and fixing it before the next major sale.

*Action:* I added detailed logging around the payment gateway calls, replicated the load in a staging environment, and traced the failures to a race condition in the session token refresh logic. I rewrote the token refresh to be idempotent and added a distributed lock using Redis.

*Result:* Checkout failures dropped to near zero in subsequent sales, and the fix was deployed with no downtime.

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Q: Tell me about a time you disagreed with a technical decision made by your team.

*Situation:* My team decided to use a NoSQL database for a reporting module that had complex, relational queries.

*Task:* I believed a relational database was the better fit and needed to make that case without derailing the sprint.

*Action:* I prepared a short comparison document showing query complexity, join requirements, and the maintenance burden of denormalising data in NoSQL. I presented it in our next design review and proposed a one-week spike to prototype both options.

*Result:* The team ran the spike and switched to PostgreSQL. The reporting module launched on time and query performance met SLA requirements from day one.

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Q: Describe a situation where you had to meet a tight deadline.

*Situation:* We had a client demo in three days and a critical API integration was incomplete because a third-party dependency was delayed.

*Task:* I had to deliver a working demo flow without the real third-party API being ready.

*Action:* I built a lightweight mock server that simulated the API responses, completed the integration on our side, and wrote a clear handover doc so the team could swap in the real API after the demo.

*Result:* The demo ran smoothly, the client signed the contract, and the real integration was completed the following week with minimal rework.

STAR for behavioural questions: Structure every story as Situation (set the context briefly), Task (your specific responsibility), Action (what YOU did, not the team), Result (a concrete outcome with a business impact if possible). Keep the full answer to about 2 minutes.

DSA questions: Think out loud from the start. State the brute-force approach first, then optimise. Confirm input constraints before coding. Walk through a small example before writing any code, and check edge cases (empty input, single element, integer overflow) before saying you are done.

System design: Use a clear structure: clarify requirements, estimate scale, define APIs, choose data storage, draw the high-level architecture, then dive into one or two components in detail. Always discuss trade-offs rather than claiming one solution is simply 'the right answer.'

Disagreement or conflict questions: Show that you rely on data and reasoning, not seniority or emotion. Frame the outcome as 'the team decided' even when you drove the change, to avoid sounding ego-driven.

VIEWS interviewers, like most product and service companies, typically look for three things beyond raw coding skill.

Problem-solving process over perfect answers. Interviewers want to see how you break down an unfamiliar problem. Thinking aloud, asking clarifying questions, and iterating on your approach signal that you can handle real-world ambiguity.

Ownership and impact. Behavioural answers that show you drove outcomes (not just participated) stand out. Use 'I' not 'we' when describing your own actions, even in team projects.

Communication clarity. Software Engineers at VIEWS work across teams and with non-technical stakeholders. Candidates who explain technical concepts plainly and listen well tend to score higher in culture-fit rounds.

Finally, genuine curiosity about the product or domain matters. Candidates report that asking thoughtful questions about the engineering challenges VIEWS is solving leaves a stronger impression than rehearsed questions about perks.

Week 1: DSA foundation. Practice array, string, and linked list problems at easy-to-medium difficulty on a coding platform. Aim for 2-3 problems a day. Focus on understanding the pattern (two pointers, sliding window, hashing) rather than memorising solutions.

Week 2: DSA depth and system design basics. Move to trees, graphs, and dynamic programming. In parallel, study one system design topic each day: URL shortener, rate limiter, notification service, search autocomplete. Practice drawing architecture diagrams and explaining trade-offs out loud.

Week 3: Behavioural prep and mock interviews. Write out 5-6 STAR stories covering: a tough technical challenge, a deadline crunch, a conflict with a teammate, a project failure and what you learned, and a time you took initiative. Do at least two mock interviews with a peer or mentor and get feedback on your communication.

Final 48 hours. Review your STAR stories once, do one or two warm-up coding problems, research VIEWS recent products or engineering blog posts if available, and prepare 3-4 thoughtful questions to ask the interviewer.

Jumping straight to code. Many candidates skip clarifying questions and start coding immediately, then realise midway that their assumption about input size or output format was wrong. Always spend 2-3 minutes aligning with the interviewer before writing a single line.

Vague behavioural answers. Saying 'I worked on a difficult project and we delivered it on time' tells the interviewer nothing. Every behavioural answer needs a specific situation and a concrete result.

Ignoring time and space complexity. Even if your code runs correctly, not discussing complexity signals that you are not thinking like a professional engineer. Always state Big-O for both time and space.

Overcomplicating system design. Candidates sometimes propose a distributed microservices architecture for a feature that could be built as a simple REST API. Start simple, then add complexity only when scale requirements justify it.

Not asking questions at the end. Saying 'I have no questions' reads as low interest. Interviewers notice it. Prepare at least two genuine questions about the team, the tech stack, or the engineering challenges ahead.