VAST Data DevOps Engineer Interview: Questions, Experience & Prep (2026)

VAST Data is a US-based company building high-performance, all-flash data infrastructure used for AI and enterprise workloads. In India, DevOps engineers at VAST Data work on cloud infrastructure, deployment pipelines, and keeping distributed storage systems reliable at scale. VAST Data currently has 247 open roles globally, reflecting strong hiring momentum into 2026.

The interview process typically spans three to five rounds. Candidates report a mix of a recruiter screen, a technical phone screen, a system design round focused on infrastructure or distributed systems, a hands-on scripting or coding round, and a final cultural or leadership discussion. Confirm the exact structure with your recruiter, as it can vary by team and location.

The knok jobradar tracked 811 DevOps Engineer openings across India as of July 2026. Bangalore leads with 187 openings, followed by Delhi (40), Pune (37), Hyderabad (28), Chennai (13), and Mumbai (11).

Salary bands across the market for DevOps roles in India:

Your specific offer at VAST Data will depend on your level, the role's hiring band, and how you negotiate.

1. Walk us through a CI/CD pipeline you designed end-to-end. What tools did you choose and what trade-offs did you make?
2. How would you design a Kubernetes-based deployment strategy for a latency-sensitive storage application?
3. VAST Data's product integrates with S3, NFS, and NVMe-over-Fabrics. How have you worked with object or file storage systems at scale, even in a supporting role?
4. Describe your approach to infrastructure as code. Which tools have you used (Terraform, Pulumi, Ansible) and in what situations did each fit best?
5. How do you handle secrets, credentials, and certificate rotation in a multi-cloud environment?
6. Tell us about a production outage or incident you owned. How did you find the root cause, and what did you put in place to prevent a recurrence?
7. How do you monitor and alert on a distributed cluster where individual node failures are expected and normal?
8. What does your observability stack look like? Walk us through how logs, metrics, and traces connect in a system you have managed.
9. How do you manage a zero-downtime Kubernetes upgrade for a stateful workload?
10. A development team wants you to speed up their deployment cycle. Walk us through how you would approach this without sacrificing stability.
11. How do you evaluate whether an infrastructure change is ready to go to production?
12. Describe a time you had to work with software engineers to fix a performance problem that crossed both application and infrastructure layers.

Q: Tell us about a production incident you owned. How did you find the root cause and what did you change afterward?

*Situation:* Our primary Kubernetes cluster began dropping requests at irregular intervals, affecting a critical service. Existing alerts were not catching the issue in time.

*Task:* I was the on-call engineer responsible for identifying the root cause, restoring service, and preventing a recurrence.

*Action:* I pulled logs and Prometheus metrics, tracing latency spikes to a node that was repeatedly failing health checks but not being evicted correctly. The eviction threshold had been misconfigured after a recent cluster upgrade. I manually cordoned the node, redistributed the affected pods, and restored traffic. I then wrote a post-mortem, corrected the eviction config, and added a synthetic test to our CI pipeline that validates node health check behaviour end-to-end before any upgrade is promoted.

*Result:* The issue did not recur. The synthetic test has since caught two similar misconfigurations in staging before they could reach production.

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Q: Walk us through a CI/CD pipeline you built from scratch.

*Situation:* Our team was deploying to production manually using shell scripts. Releases were slow, error-prone, and no one had clear visibility into what was running where.

*Task:* I was asked to design and implement an automated pipeline to deploy microservices to a Kubernetes cluster reliably.

*Action:* I set up GitLab CI with Helm charts for packaging and ArgoCD for GitOps-style continuous delivery. I added automated smoke tests as a quality gate before any production promotion and configured separate pipelines for feature branches and the main branch, with environment-specific secret injection via Vault.

*Result:* Deployments that previously required manual coordination now run automatically. Teams reported going from slow, error-prone manual releases to fast automated deploys. Rollbacks that previously required escalating to multiple engineers became a single click in ArgoCD.

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Q: Describe a time you worked across infrastructure and application layers to fix a performance problem.

*Situation:* A data ingestion service was intermittently timing out under load, but the application team reported their code looked fine.

*Task:* I needed to determine whether the problem was in the application, the network, the storage layer, or the Kubernetes scheduling.

*Action:* Using 'kubectl top', node exporter metrics, and packet captures, I narrowed the issue to disk I/O saturation on nodes running both the ingestion pods and a logging sidecar writing aggressively to disk. I worked with the application team to switch the sidecar to async batched writes and moved the ingestion pods to a node pool with faster local SSDs.

*Result:* Timeouts dropped to near zero under the same load conditions. The application team also added disk I/O metrics to their service dashboards, improving their ability to catch similar issues independently in the future.

Use STAR for every behavioural question. STAR stands for Situation, Task, Action, Result. Keep the Situation and Task brief. Spend most of your answer on Action, making clear what you specifically did rather than what the team did as a whole. Close with a concrete Result. If you cannot name an exact figure, describe the before and after state in qualitative terms.

For system design, start with requirements. Ask clarifying questions before drawing anything: scale, latency targets, consistency needs, failure tolerance. VAST Data interviewers often focus on how you handle failure at the storage or network layer, so address redundancy and recovery proactively rather than waiting to be prompted.

For scripting or coding rounds, think out loud. Talk through edge cases before writing a single line. VAST Data products serve enterprise clients where reliability is non-negotiable, so interviewers pay close attention to whether you consider failure paths, not just the happy path.

For tool questions, lead with the problem, not the tool. Saying 'I chose Terraform because my team already knew it' signals weak reasoning. Saying 'I chose Terraform over Pulumi because we needed a large community of modules and our team lacked Go skills' shows genuine trade-off thinking, which is what interviewers are actually evaluating.

Deep infrastructure ownership. Interviewers want to see that you treat infra as a product: you monitor it proactively, improve it continuously, and own incidents end-to-end rather than escalating and forgetting.

Comfort near the storage layer. VAST Data builds storage products, so even in a DevOps role, candidates report that interviewers value familiarity with object storage, NFS mounts, disk I/O profiling, and latency at the hardware layer. You do not need to be a storage engineer, but treating storage as a black box will hurt you here.

Kubernetes depth, not breadth. Knowing many tools at a surface level matters less than knowing Kubernetes and one cloud platform thoroughly. Expect questions that go well beyond 'what is a pod' into scheduler behaviour, resource limits, node affinity, and StatefulSets.

Structured, clear communication. VAST Data serves large enterprise clients. Engineers are expected to explain complex infra issues to non-technical stakeholders. Candidates report that interviewers watch closely for how you structure explanations, especially under pressure in a technical round.

A default bias toward automation. Every manual step should feel uncomfortable to you. Interviewers quickly notice when a candidate describes automation as something they do 'when there is time' rather than as the default starting point.

Week 1: Deepen your core skills. Review Kubernetes thoroughly: pod scheduling, resource quotas, rolling updates, StatefulSets, and node affinity. Practice writing Terraform modules for a basic cloud resource. Refresh your Linux performance tools: 'top', 'iostat', 'netstat', and 'tcpdump'.

Week 2: Build your story bank. Write out five or six work situations using STAR. Cover at minimum: a production incident you owned, a CI/CD pipeline you designed, a cross-team collaboration, a performance debugging session, and a process improvement you drove. VAST Data questions typically draw from all of these areas.

Week 3: Practice storage and observability. Read publicly available material on NVMe-over-Fabrics, S3-compatible storage APIs, and NFS. Set up a monitoring stack (Prometheus plus Grafana, or a similar open-source combination) in a free-tier cloud account. Practice walking through your observability setup as if explaining it to an interviewer.

Final days: Mock interviews and company research. Do at least two mock technical interviews with a peer. Research VAST Data's product line, recent news, and any publicly available engineering content. Candidates who reference VAST Data's actual product context in their answers tend to stand out.

If you are actively applying alongside your preparation, knok checks 150+ job sites nightly, applies to roles that match your resume, and messages HR for you, so you can keep your focus on interview prep rather than searching.

Vague answers with no specifics. 'I improved deployment speed' means nothing without context. Always say what you changed, how you measured the outcome, and what the result was. If you lack exact figures, describe the before and after state in qualitative terms.

Skipping failure paths in system design. Designing only for the happy path is a red flag at a storage-focused company. Always address what happens when a node dies, a disk fills, or a network partition occurs. Raise these scenarios proactively rather than waiting for the interviewer to push you.

Over-claiming team achievements. 'We reduced downtime' gives context, but the interviewer wants to know what you specifically did. If you led the effort, say you led. If you contributed one component, say that clearly.

Treating storage as someone else's problem. DevOps engineers at a storage company are expected to have working knowledge of I/O patterns, disk performance, and storage protocols. Saying 'I just mount the volumes and leave the rest to the storage team' will not land well in this interview.

Ignoring the 'why' behind tool choices. Every tool question is a trade-off question in disguise. If you cannot explain why you chose Helm over Kustomize, or one monitoring tool over another, the interviewer cannot properly assess your engineering judgement.

Jumping into system design without scoping it first. Skipping clarifying questions before designing a system signals that you may do the same on the job. Start every system design with at least two questions about scale, latency, and failure requirements.