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

Glean builds enterprise AI search, helping large companies find information across their internal tools. With 146 open roles on the knok jobradar right now, the company is scaling fast, and DevOps engineers sit at the core of that growth. You would be working on the infrastructure that keeps Glean's indexing pipelines, connectors, and search serving layer running reliably for enterprise clients who expect near-zero downtime.

The interview process typically covers a recruiter screen, one or two technical rounds focused on infrastructure design and Kubernetes, and a final round that may include cross-functional or behavioral discussion. Candidates report the full loop taking two to three weeks. Expect questions around CI/CD, Kubernetes, observability, secrets management, and distributed systems reliability.

Across India, the knok jobradar counted 811 DevOps Engineer openings as of July 2026. Bangalore leads with 187 roles, followed by Delhi (40), Pune (37), Hyderabad (28), Chennai (13), and Mumbai (11). Salary bands from the knok jobradar put the range at 6-12 LPA for entry level (0-2 years), 15-28 LPA for mid-level (3-5 years), 30-50 LPA for senior (6-9 years), and 45-70+ LPA for lead or staff positions. Glean, as a well-funded enterprise SaaS product, typically targets the upper portion of these bands.

1. Walk us through how you would design a CI/CD pipeline for a microservices platform like Glean.
2. How do you handle secrets management in a Kubernetes-based environment?
3. Glean indexes enterprise data continuously. How would you design an on-call process and incident response runbook for a pipeline that cannot afford extended downtime?
4. Describe your experience with infrastructure as code. Which tools have you used and why did you choose them?
5. How do you approach monitoring and alerting for a distributed system with many moving components?
6. Explain how you would set up autoscaling for a workload that has both CPU-bound and I/O-bound components.
7. Glean integrates with tools like GSuite, Slack, and Confluence. How would you think about reliability and retry logic for third-party connector infrastructure?
8. How do you ensure container image security from build to deployment?
9. Walk us through a situation where you reduced cloud infrastructure costs without hurting reliability.
10. How do you manage Kubernetes cluster upgrades with zero downtime for production workloads?
11. How would you approach multi-tenant isolation at the infrastructure level for an enterprise SaaS product?
12. Describe how you approach capacity planning for a system where indexing load spikes unpredictably.

Q: Walk us through how you would design a CI/CD pipeline for a microservices platform like Glean.

*Situation:* At my previous company, we were running fifteen-plus microservices with separate release cycles and no unified pipeline. Deployments were manual and error-prone, causing frequent rollback incidents.

*Task:* I was asked to build a CI/CD system that would let each team deploy independently, with consistent quality gates and full audit trails.

*Action:* I designed a GitOps workflow using GitHub Actions for CI and ArgoCD for continuous delivery to Kubernetes. Each service repository had its own pipeline: lint and unit tests ran first, then container image build and push to a private registry with image signing. A separate infrastructure repository held Helm charts and environment-specific values. ArgoCD watched that repository and synced changes to staging automatically, while production required a manual promotion step with approval. I added Snyk for vulnerability scanning and Slack notifications for failed deployments.

*Result:* Deployment frequency increased from once a week to several times a day per service. Rollback time dropped to under five minutes because ArgoCD could revert a sync in seconds. The team reported significantly less manual toil during release windows.

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Q: Glean's platform cannot afford extended downtime. Describe how you have handled a high-severity production incident.

*Situation:* Our search indexing service went down during peak business hours at my previous company. Customers could not search their internal data, and we had no runbook for this specific failure mode.

*Task:* As the on-call engineer, I had to triage the issue, communicate status to stakeholders, and restore service simultaneously.

*Action:* I checked our Grafana dashboards and traced the issue to a Kafka consumer lag spike caused by a misconfigured partition rebalance after a node restart. I scaled the consumer group horizontally using kubectl, which started reducing lag. I opened a war-room call, posted status updates every fifteen minutes to the engineering Slack channel, and looped in the data platform team to verify message ordering was intact. Once lag cleared, I confirmed search results with a quick smoke test. After the incident, I wrote a detailed postmortem and added a Kafka consumer lag alert with a pre-written runbook so the next responder would not start from scratch.

*Result:* Total downtime was about forty minutes. The postmortem was shared company-wide and led to two new alerts adopted by other teams. We had zero recurrence of that specific failure in the following six months.

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Q: Tell us about a time you reduced infrastructure costs without sacrificing reliability.

*Situation:* Our Kubernetes cluster was over-provisioned after a big customer launch. The expected traffic never came, but the nodes stayed at their peak-provisioned size.

*Task:* I was asked to reduce the monthly cloud bill while keeping our SLAs intact.

*Action:* I ran a two-week profiling exercise using Prometheus and Kubernetes metrics to compare actual CPU and memory consumption against requested resources. Most services had requests set to two to three times their actual peak usage. I worked with each service team to right-size their resource requests and limits, moved non-critical batch jobs to spot instances, and enabled cluster autoscaler with a tighter scale-down policy. I also identified three idle namespaces from deprecated features and cleaned them up.

*Result:* Cloud spend dropped meaningfully over the following billing cycle. Our SLA compliance stayed at its target level through the next quarter, and the right-sizing templates I created became the standard for new service onboarding at the company.

For technical design questions (CI/CD, Kubernetes, observability): lead with the constraint or failure mode you were solving for, not the tool you chose. Interviewers at product companies like Glean want to hear your reasoning before your solution.

For incident and reliability questions: the STAR format maps cleanly. What was the system state (Situation), what was your role (Task), how did you triage and communicate (Action), and what was the business outcome plus what changed afterward (Result). Always end with what you improved so the same incident does not repeat.

For cost and efficiency questions: show that you measured before you acted. 'I looked at the metrics first' signals engineering discipline over gut decisions.

For behavioral questions: use the same STAR structure but emphasize how you communicated technical constraints to non-technical stakeholders. Glean sells to enterprise clients, so cross-team communication is a genuine part of the job.

For system design: state your assumptions, define your reliability targets, then walk through compute, storage, networking, and observability in order. This shows structured thinking even if you do not know Glean's exact architecture.

Glean's platform is always-on for enterprise clients, so interviewers prioritize a reliability-first mindset. They want to see that you think about failure modes before they happen, not just after.

Security awareness is a real differentiator. Glean's customers are often large enterprises with strict compliance requirements. Expect follow-up questions on secrets management, least-privilege RBAC, image scanning, and audit logging. Show that security is part of your default workflow, not an afterthought.

Structured communication matters. Candidates report that Glean interviewers respond well to clear, reasoned answers. If you do not know the exact tool or Glean's specific stack, say so and reason from first principles. 'I have not used X but here is how I would approach it' lands better than vague or overconfident answers.

Ownership. Glean emphasizes this value publicly. Stories where you identified a gap, fixed it without being asked, and shared the outcome broadly tend to land well in behavioral rounds.

Week 1: Kubernetes fundamentals. Be able to explain pod scheduling, resource requests and limits, HPA and VPA, cluster autoscaler, RBAC, and network policies. Practice kubectl commands from memory. Review common troubleshooting scenarios like CrashLoopBackOff, pending pods, and OOMKilled.

Week 2: CI/CD and GitOps. Be comfortable explaining GitOps versus push-based deployments, and have a concrete example of a pipeline you built or improved. Review ArgoCD or Flux if you have not used them. Be ready to sketch a pipeline design in a shared doc during the interview.

Week 3: Observability. Know the difference between metrics, logs, and traces. Be able to walk through how you would instrument a new service and set up meaningful alerts, not just simple CPU or memory thresholds. Practice explaining SLOs and error budgets in plain language.

Week 4: System design practice. Pick a reliability scenario (connector pipeline failure, search index rebuild, multi-tenant isolation) and walk through your design out loud. Glean's business context gives you realistic prompts to work with.

Throughout: Read Glean's engineering blog and any public talks from their team. Glassdoor and Blind typically carry candidate-reported interview experiences that can give you a sense of current question themes. If you want to stay on top of new Glean openings while you prep, knok checks 150+ job sites nightly, applies to roles matching your resume, and messages HR for you.

Describing tools instead of decisions. Saying 'I used Terraform and Ansible' without explaining why or what problem it solved is a common miss. Interviewers want the reasoning, not just the tech stack list.

Skipping the 'what changed afterward' step. Many candidates stop at 'we fixed the issue.' Glean cares about whether you improved the system so the incident does not repeat. Always end with the postmortem, the runbook, or the alert you added.

Over-engineering system design answers. Proposing a highly complex architecture in the first few minutes without asking about scale, budget, or constraints signals poor judgment. Start simple, state your assumptions, and add complexity only when asked.

Ignoring security. Candidates who answer infrastructure questions without mentioning secrets management, least-privilege access, or image scanning miss an important signal for a company serving enterprise clients.

Skipping behavioral prep. DevOps interviews at product companies typically include two to three behavioral questions. Candidates who only prepare technical answers often stumble when asked about conflict resolution or cross-team collaboration.