DevOps Engineer Jobs
3 genuine, current openings aggregated from public job boards — Remotive, Jobicy, Arbeitnow and RemoteOK. JobStraight never reposts stale listings: each card links straight to the original posting. Before you apply, paste the job into TrueFit to see your honest fit score, tailor your resume in Resume Studio, and rehearse with AceCoach — all free.
Are you a talented Senior DevOps looking for a remote job that lets you show your skills and get decent compensation? Look no further than Lemon.io — the marketplace that connects …
Entwickle mit uns die Zukunft! aurivus ist ein innovatives Startup, das sich auf die Entwicklung einer hochmodernen KI zur 3D- Datenverarbeitung in Web-Anwendungen spezialisiert ha…
Das erwartet dich bei uns: Du arbeitest in Beratungs- und Softwareentwicklungsprojekten bei Kunden wie AXA, BMW, der Deutschen Bahn und ZDF Du berätst zu Cloud-Native-Architekturen…
Before you apply
Application volume is the defining feature of this market. LinkedIn has been reported to process around 11,000 job applications per minute — roughly a 45% year-on-year rise — and recruiters describe receiving 300–500 applications on a popular role within three days. The practical consequence is that being a plausible candidate is no longer enough; you need to be an obvious one for the specific posting.
Two checks are worth doing before every application. First, the knockouts — work authorisation, years of experience, location and on-site expectations are filterable fields, and failing one ends the application regardless of how strong the rest is. Second, keyword coverage: make sure every skill you genuinely have that the posting names appears in your resume in the posting's own words. That is coverage, not density, and it never means adding skills you don't have. The widely-repeated claim that ATS software auto-rejects 75% of resumes is a myth traceable to a 2012 sales pitch — what actually filters you is those knockout fields plus a recruiter's six-to-eight-second scan, as our ATS guide explains with sources.
DevOps Engineer interview questions you should be ready for
These are questions that recur in devops engineer interviews, with the structure of a strong answer. They're from our own question bank — not scraped from review sites.
How would you design a rate limiter? System design
- Clarify the limit: per user, per IP, per endpoint, and the window.
- Fixed window is simplest but allows bursts at boundaries.
- Sliding window log is accurate but memory-heavy; sliding counter is a good middle ground.
- Token bucket allows controlled bursts — usually the best default.
- Store counters in a shared fast store (Redis) so it works across instances; return 429 with Retry-After.
Watch out: Designing a per-instance limiter that breaks the moment you scale out.
At senior level: Discuss distributed accuracy vs latency, and degrading open vs closed.
When would you use a message queue? Conceptual
- To decouple producer from consumer so a slow consumer can't block the request.
- To smooth spikes — the queue absorbs burst load.
- For work that can be async: emails, thumbnails, exports.
- For retries and dead-letter handling on failure.
- Cost: eventual consistency, ordering concerns, and duplicate delivery you must handle.
Watch out: Adding a queue where a simple synchronous call would do.
At senior level: Discuss at-least-once semantics, idempotent consumers and DLQ alerting.
Explain caching strategies and cache invalidation. Conceptual
- Cache-aside: app checks cache, on miss reads DB and populates — most common.
- Write-through keeps cache and DB in sync on write; write-behind defers the DB write.
- Set TTLs so staleness is bounded even if invalidation is missed.
- Invalidate explicitly on write for data that must be fresh.
- Watch for stampedes — use locks or staggered TTLs.
Watch out: Caching without a plan for stale data.
At senior level: Discuss layered caches (CDN/app/DB) and measuring hit ratio against cost.
How would you scale a service from 100 to 1 million users? System design
- Measure first — find the actual bottleneck, don't guess.
- Vertical scale and query/index tuning buy early headroom cheaply.
- Go stateless and scale horizontally behind a load balancer.
- Add caching and a CDN; move slow work to queues.
- Then split the data: read replicas, then sharding. Add monitoring at every step.
Watch out: Leaping straight to microservices and Kubernetes.
At senior level: Sequence changes by cost/benefit and discuss the organisational cost of each.
Explain the CAP theorem. Conceptual
- Consistency, Availability, Partition tolerance — you can't have all three.
- Networks partition in reality, so P is non-negotiable.
- The real choice is CP (refuse writes to stay correct) or AP (serve possibly-stale data).
- Banking leans CP; a social feed leans AP.
- Note it's per-operation, not per-database.
Watch out: Treating it as a permanent product-wide label.
At senior level: Bring in PACELC — the latency/consistency trade-off when there's no partition.
What is CI/CD and what belongs in a good pipeline? Conceptual
- CI: every merge is automatically built and tested against main.
- CD: passing builds deploy automatically to an environment.
- A good pipeline runs lint, unit tests, integration tests and a security scan.
- It builds one immutable artifact and promotes that same artifact across environments.
- It's fast (minutes) or people route around it, and it can roll back.
Watch out: Describing a pipeline with no rollback plan.
At senior level: Discuss trunk-based development, deployment frequency and change-failure rate.
Predict the full question set for a specific job description →
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