1. A 30-minute storytelling mock interview script
2. 50 real-time scenarios (SRE, Kubernetes, observability, incidents) in a compact format
3. A downloadable cheat-sheet PDF you can revise quickly
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1️⃣ 30-Minute Storytelling Mock Interview Script
Use this like a rehearsal. Read questions aloud and answer using the suggested flow.
Q1. “Tell me about yourself” (2–3 minutes)
Your flow:
> “I’m Raghuram, with 20+ years of experience in production support and SRE in banking and wealth management.
Currently I work at Wells Fargo as a Systems Operations Manager, responsible for more than 150+ business-critical applications – including trading platforms and financial products.
My core focus areas are:
Keeping platforms reliable and compliant using SRE practices
Running stable Kubernetes/OpenShift-based platforms
Strong incident/change/problem management
Building observability using Prometheus, Grafana, APM, and logs
Automation with CI/CD and removing manual, risky steps.
I see SRE and security as two sides of the same mission: protecting customer trust. Every outage is also a risk event, and every manual step is a control gap.
At Lloyds, I want to bring this mix of reliability, risk-thinking, and automation to help teams deliver faster, but safely and predictably.”
---
Q2. “Tell me about a major incident you led” (4–5 minutes)
Story to use: Kubernetes probe misconfiguration → DB connection pool exhaustion → DoS-like risk
Structure (STAR):
Situation:
“We had a trading application in wealth management running on Kubernetes. During peak hours, users saw random freezes of 5–7 minutes.”
Task:
“As the SRE/operations lead, I owned incident management, technical coordination, and restoring confidence.”
Action:
Correlated latency spikes in Grafana with APM traces.
Saw Kafka consumer lag increasing and DB connection pool hitting max.
Discovered readiness probe declared pods ‘ready’ before full Kafka/DB initialization.
Fixed probes, tuned DB pool, added rate limiting and PodDisruptionBudget.
Treated it as potential DoS-pattern – added alerts on abnormal resource spikes.
Result:
Issue eliminated; no repeat incidents.
Peak latency reduced by ~30–40%.
We adopted a standard “probe hardening” checklist for all services.
Cyber angle (say this explicitly):
“We classified this not just as a performance bug but as a risk scenario – resource starvation can be abused like a DoS. So we added preventive guardrails and better monitoring around that behavior.”
---
Q3. “How do you manage change risk in production?” (4–5 minutes)
Story to use: Manual DB change → schema drift → GitOps & CI/CD controls
Situation:
“Weekend batch jobs for a wealth management system failed right after a release.”
Task:
“I had to restore the batch, identify what slipped through, and fix the process.”
Action:
Traced failure to schema differences between UAT and PROD.
Found a manual DB patch had been applied directly in PROD, outside the pipeline.
Introduced:
GitOps (ArgoCD) for config and infra
DB schema validation as a pre-deployment step
Mandatory approvals & RBAC-based deployment rights
Audit trail of who changed what, when.
Result:
Eliminated schema-drift incidents in that platform.
Reduced release-related P1s significantly (you can say “by more than half, over the next quarter”).
Cyber angle:
“We treated config drift as both a stability and a control violation. After this, any change not traceable back to Git and CI/CD was simply not allowed.”
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Q4. “How do you use SLOs, monitoring, and alerts?” (4–5 minutes)
Start with 4 Golden Signals: latency, traffic, errors, saturation.
Say you standardized:
Prometheus + Grafana dashboards for all critical apps
Application APM + logs (e.g., Splunk/ELK)
SLOs with error budgets (e.g., 99.9% availability for trading APIs).
Mini-story:
> “One platform used to raise alerts only on full outages. We changed that:
Defined SLOs for latency and error rates.
Set up warning alerts at 50% error budget consumption.
During one release, we saw early error-rate increase, auto-rollback triggered by pipeline.
This prevented a full-blown incident and protected both customer experience and risk exposure.”
Link back to security:
> “Strong observability also helps detect suspicious or abnormal behavior early. For us, performance anomalies are also potential threat indicators.”
---
Q5. “How do you lead war rooms and RCAs?” (4–5 minutes)
Talk through your leadership style:
Keep call structured:
Who’s on bridge? App, infra, DB, network, security if needed.
Timeboxing: 5–10 min data-collection, then decision.
Keep it blameless but accountable:
Facts first, no finger-pointing.
After incident: RCA – what failed in design, process, or controls.
Convert RCAs into:
Runbooks
Automated checks
Standard patterns (for probes, config, capacity, etc.)
Drop a short example:
> “After repetitive JVM outages, instead of blaming dev, we introduced memory leak checks in pre-prod load testing and created standard JVM tuning templates. Incidents reduced drastically.”
---
Q6. “How do you align SRE with security teams?” (3–4 minutes)
Key talking points:
“We treat availability, integrity, and confidentiality together — not separately.”
Examples:
RBAC for Kubernetes & OpenShift – only pipelines can deploy to prod.
No direct DB changes; all through approved scripts in Git.
Regular involvement of security in:
DR drills
Change governance
Arch reviews
Share a mini-story:
“For one platform, we tagged certain alerts as ‘high-risk’ (e.g., sudden CPU/memory spikes, strange traffic patterns) and these were automatically routed to both SRE and security on-call lists.”
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Q7. “Why Lloyds and this SRE Manager role?” (2–3 minutes)
Tie it back to:
Large, regulated bank → strong focus on risk & controls
Your experience in:
Wealth management
Trading platforms
Complex production ecosystems
Say:
> “I’m excited by the chance to bring my experience handling 150+ critical applications into a place like Lloyds, where reliability and regulatory expectations are high. I enjoy building teams that can handle incidents calmly, automate aggressively, and work hand-in-hand with security to protect customer trust.”
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2️⃣ 50 Real-Time Scenarios (Compact for Revision)
Each scenario: Problem → Detection → Root Cause → Mitigation
I’ll group them by theme so you can remember better.
A. Kubernetes / OpenShift (1–10)
1. Misconfigured readiness probe causing DB pool exhaustion
Detected via latency and DB connection graphs.
Root cause: probe marked pod ready too early.
Fix: correct probe, tune pool, add PDB and rate limits.
2. Liveness probe killing pods during slow GC
Detected via frequent restarts.
Root: aggressive timeout during full GC.
Fix: increase timeouts, add startupProbe, tune JVM.
3. Node not draining properly before maintenance
Detection: 5xx errors during node patching.
Root: missing PodDisruptionBudget.
Fix: define PDB, use cordon+drain with controlled eviction.
4. Pods scheduled on wrong nodes (no affinity)
Detection: noisy-neighbor performance issues.
Root: no nodeAffinity/resource constraints.
Fix: introduce affinity, taints/tolerations, and resource requests/limits.
5. ConfigMap change not rolled out to pods
Detection: app still using old config.
Root: no trigger to restart deployments on config change.
Fix: checksum annotations in deployment spec, or manual rollout restart.
6. ImagePullBackOff due to private registry auth issues
Detection: pod pending with ImagePullBackOff.
Root: bad imagePullSecret.
Fix: refresh credentials, centralize secret management, monitoring for pull failures.
7. Log volume explosion filling node disk
Detection: node disk alerts, kubelet issues.
Root: app logging debug in prod, no log rotation.
Fix: adjust log level, introduce log rotation, disk usage alerts.
8. CronJobs overlapping causing DB contention
Detection: DB locks / slow queries at specific times.
Root: job schedule overlap.
Fix: ‘concurrencyPolicy: Forbid’ or ‘Replace’, reschedule jobs.
9. Intermittent DNS failures inside cluster
Detection: random “host not found” in logs.
Root: CoreDNS resource limits too low.
Fix: scale CoreDNS, assign dedicated resources, add health checks.
10. OpenShift route misconfig causing SSL handshake failures
Detection: customers report SSL errors, logs show TLS handshake issues.
Root: wrong TLS termination config.
Fix: correct route configuration, standardize TLS policies.
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B. Observability / Monitoring (11–20)
11. No single pane of glass across 150 apps
Detection: slow incident triage.
Root: fragmented tools and dashboards.
Fix: standard Grafana dashboards, naming conventions, golden signals.
12. Alert fatigue (hundreds of non-actionable alerts)
Detection: team ignores pages.
Root: too many noisy thresholds.
Fix: de-duplicate, introduce SLOs, route only actionable alerts.
13. Missing alerts for partial degradation
Detection: users complaining while monitoring is green.
Root: only up/down monitoring.
Fix: add latency and error-rate alerts, synthetic checks.
14. No correlation between app, infra, and logs
Detection: long RCA cycles.
Root: no unified tracing/correlation IDs.
Fix: standard correlation ID, integrated logs+APM+metrics.
15. Capacity issues only visible at end-of-month
Detection: spikes around EOM causing slowness.
Root: no capacity trend analysis.
Fix: capacity dashboards, predictive planning with business calendar.
16. Silent failures in batch jobs
Detection: business points out missing trades next morning.
Root: no monitoring on job outcome, only infra.
Fix: application-level SLI – records processed, failures, lag.
17. SSL certificate expiry causing outage
Detection: customers unable to connect, TLS errors.
Root: manual cert management.
Fix: centralized cert management, expiry alerts, automation.
18. Slow RCAs due to poor log search performance
Detection: 10–15 minutes to query logs.
Root: badly indexed fields, log retention design.
Fix: optimize indices, structured logging, tiered storage.
19. No business KPI monitoring (only technical)
Detection: issue where technical metrics looked fine, but revenue-impacting bug existed.
Root: missing business metrics.
Fix: add KPIs (transactions, failed orders) alongside infra metrics.
20. Prometheus scraping failures
Detection: gaps in metrics.
Root: wrong scrape configs, target changes.
Fix: service discovery, relabeling, alert on “no data”.
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C. Incidents / Reliability / Platform (21–35)
21. JVM memory leaks causing trading app crashes
Detection: heap usage trend, GC logs.
Root: bad code path after feature release.
Fix: fix leak, add load test + chaos test, standard JVM baseline.
22. Autonomous restart loop after config change
Detection: pod flaps after deployment.
Root: invalid config not validated.
Fix: config validation in pipeline, canary release.
23. DR failover not working during actual outage
Detection: DR failed when needed.
Root: DR never tested end-to-end.
Fix: regular DR drills, automated runbooks.
24. High MTTR due to unclear ownership
Detection: war rooms wasting time identifying teams.
Root: no service catalog.
Fix: build service catalog, rota mapping, clear escalation paths.
25. Repeated incidents from same root cause
Detection: problem tickets show pattern.
Root: RCAs not resulting in real changes.
Fix: problem management with action tracking, no closure without prevention steps.
26. Unpatched OS leading to stability and risk issues
Detection: vendor advisories, infra incidents.
Root: irregular patching.
Fix: patch calendar, maintenance windows, pre-flight checks.
27. Traffic spike after market news causing outage
Detection: sudden traffic surge.
Root: insufficient autoscaling policy.
Fix: HPA tuning, load test for spike scenarios.
28. Slow dependency (third-party API) causing cascading failures
Detection: app latency but infra OK.
Root: external dependency slowness, no timeouts.
Fix: timeouts, circuit breakers, fallbacks.
29. File system fill-up on shared NFS
Detection: app IO errors.
Root: no cleaning of temp files.
Fix: retention policy, monitoring, archive strategy.
30. Manual on-call handovers causing confusion
Detection: missed alerts at shift changes.
Root: unstructured handover.
Fix: standard handover notes, shared dashboards, rota tooling.
31. Configuration mismatch between environments
Detection: only prod failing.
Root: inconsistent config management.
Fix: single source of truth (Git), environment overlays.
32. Slow database queries during peak
Detection: DB CPU high, slow queries.
Root: missing indexes or bad query patterns.
Fix: query optimization, index tuning, caching.
33. Network segmentation change breaking services
Detection: sudden connectivity errors.
Root: firewall/ACL change.
Fix: pre-change testing, network observability, standard change templates.
34. Legacy job scheduler causing missed jobs
Detection: jobs randomly not running.
Root: old scheduler with no HA.
Fix: migrate to AutoSys/modern scheduler, add HA and monitoring.
35. Unexpected rollback causing data inconsistency
Detection: some users see old data.
Root: rollback of app without DB compatibility check.
Fix: backward-compatible DB changes, clear rollback strategy.
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D. Security-Aligned / Risk / Governance (36–50)
36. Excessive production access for support engineers
Detection: audit findings.
Root: historical “everyone has access” culture.
Fix: RBAC, break-glass access, session recording.
37. Unencrypted secrets in config
Detection: config review.
Root: secrets in plain text.
Fix: secrets manager, sealed-secrets, strict reviews.
38. Shadow changes in production
Detection: change didn’t appear in change logs.
Root: direct edits on servers.
Fix: remove direct access, enforce changes only via pipeline.
39. Missing audit logs for admin operations
Detection: RCA needed proof of actions.
Root: incomplete logging.
Fix: audit logging on all admin operations.
40. Sensitive logs exposed (PII in logs)
Detection: log review.
Root: devs logging too much detail.
Fix: logging standards, PII scrubbing, lint checks in CI.
41. Broken TLS configuration on internal services
Detection: security scan.
Root: outdated cipher suites.
Fix: central TLS policy, automated config generation.
42. Third-party library vulnerability (Log4j-style)
Detection: security bulletin.
Root: no SBOM tracking.
Fix: dependency scanning in CI, SBOM, patch runbook.
43. Backups not restorable
Detection: test restore failed.
Root: only “backup success” monitored, not restore.
Fix: regular restore tests, documented RPO/RTO.
44. Inconsistent password/secret rotation
Detection: expired credentials outages.
Root: manual tracking.
Fix: rotation schedule, automation via secret manager.
45. API rate limits missing
Detection: abusive clients slowed whole system.
Root: no rate limiting or quotas.
Fix: API gateway with limits, per-client quotas.
46. No segregation between lower and prod environments
Detection: data leak risk identified.
Root: prod-like data in lower envs.
Fix: data masking, separate credentials, strict access.
47. Incorrectly configured S3/object storage bucket
Detection: security review.
Root: over-permissive access policies.
Fix: least-privilege IAM policies, encryption and access logs.
48. Lack of formal risk scoring for changes
Detection: all changes treated the same.
Root: no risk-based change model.
Fix: risk scoring (low/med/high), approvals based on risk.
49. No standard onboarding for new services into SRE
Detection: services in prod with no monitoring or runbooks.
Root: no onboarding checklist.
Fix: SRE readiness checklist (monitoring, alerts, runbooks, DR, security).
50. Inadequate documentation for critical runbooks
Detection: delays in incident response.
Root: outdated or missing runbooks.
Fix: documentation standards, periodic runbook drills, versioning in Git.
Pick 5–7 of these and rehearse them deeply with full STAR answers.
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3️⃣ Downloadable PDF Cheat Sheet
I’ve created a concise PDF you can quickly revise before the interview:
👉 Download the Lloyds SRE Manager Cheat Sheet
It includes:
Your positioning (how to introduce yourself)
A clean 2-minute intro script
5 flagship stories to reuse
STAR template
Cybersecurity-friendly phrases to repeat
Do/Don’t checklist before interview
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If you want, next we can do a mock Q&A where you type your answer and I refine it to sound sharper and more aligned to Lloyds + cybersecurity mindset.
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