Five years of tooling progress have made it easy to collect telemetry and hard to make sense of it. A mid-sized company can now produce more metrics, logs and traces than its engineers will ever read, while its services depend on cloud regions, third-party APIs and, increasingly, model providers that it does not control. The site reliability engineer skills in demand today have less to do with installing an observability stack and more to do with deciding what to measure, what to ignore and how systems should behave when something they rely on goes wrong.
The title has also spread faster than the practice, so plenty of CVs say SRE while describing operations or DevOps work. To help you tell them apart, this post lists eight technical skills with hands-on tests, the working qualities that matter on a rota, how demand is changing, what to expect at each level and how to structure the assessment.
Core technical skills for a site reliability engineer
Measuring what users experience
Good SLIs come from thinking about where a measurement is taken. Figures from the service itself miss requests that never reached it, so strong SREs compare server, load balancer and client-side data and know what each one cannot see. How to check it: show a dashboard in which every SLO is green on a morning when support is flooded with complaints. Ask how that can be true. Good answers consider failures upstream of the measuring point, a single user journey hidden within an average and an SLI that treats a useless response as a success.
Burn rate alerting
Alerting on the rate at which the error budget is consumed is more accurate than static thresholds, but it needs real understanding to set up. Strong engineers combine a long and a short window so that alerts fire quickly on sharp failures, still catch slow leaks and clear soon after recovery. How to check it: ask them to explain why one alert needs two time windows. Then ask how they would handle a low-traffic service, where a few failed requests can look like a catastrophe. Synthetic traffic or grouping services together are sensible replies.
Telemetry design and cardinality
OpenTelemetry is now the common way to instrument services, with Prometheus and Grafana often on the receiving end. Skill lies in context propagation, sampling choices and keeping costs under control. How to check it: say that Prometheus memory jumped after a developer added a user ID label to a request metric. Ask why, and where that detail should go. Each unique label value creates a new time series, so user-level detail belongs in traces or logs, and a seasoned SRE explains this easily.
Failure behaviour between services
Many large outages start small and spread through retries. Good SREs understand timeouts, exponential backoff with jitter, circuit breakers, load shedding and graceful degradation. How to check it: describe three service layers that each retry a slow dependency several times. Ask what happens to that dependency when it starts to struggle. You want them to work out that the load multiplies, then suggest retry budgets, timeouts that shrink along the call chain and a way to serve something useful while degraded.
Capacity and traffic spikes
Autoscaling helps, but it reacts after demand arrives. Indian consumer businesses know this well, since a festival sale or a cricket final can bring a sudden surge. Strong SREs load test realistic journeys, watch saturation and identify the first real bottleneck. How to check it: say that a push notification brings a surge within moments, and new pods take a few minutes to become ready. Ask how they would prepare. Look for scaling up ahead of the event, staggered notifications, shedding non-essential work and checking databases as well as stateless services.
Linux and container performance
When a graph says nothing useful, an SRE needs to go a level lower. That means understanding CPU, memory, disk and network saturation, along with how containers are limited by cgroups. How to check it: describe a Kubernetes service with latency spikes despite low average CPU. Ask what they would look at. CPU throttling from tight limits is a classic cause, and experienced engineers will mention throttling metrics and the way averages conceal short bursts.
Safe automation
SREs write software in Python or Go, and that software can do damage at machine speed. Good automation has rate limits, dry runs and a clear stop condition. How to check it: propose a bot that restarts any pod failing its health check. Ask when it would make an outage worse. A shared dependency failure could restart everything at once, so strong candidates add limits, a pause when too many targets are unhealthy and an audit trail.
Incident command
Technical skill alone does not run a major incident. Someone must hold the overview, assign work, manage communication and decide when to mitigate. How to check it: give them a short transcript from an anonymised incident channel, with three people chasing the same theory and no customer update. Ask what they would have changed, and when. Look for named roles, a regular update rhythm and a focus on restoring service first.
Working skills that separate good from average
- Influencing without authority. SREs rarely own the product roadmap. Good ones use data from error budgets and incidents to persuade product leaders, and they pick their battles.
- Writing for readers who were not there. A clear incident summary lets a new engineer understand a failure years later. Strong SREs write these for learning, and share them widely.
- Protecting the rota. Burned-out engineers make poor decisions and then leave. Good SREs track pager load, raise it with managers and push work back to service owners when needed.
- Staying curious about near misses. The incident that almost happened is cheap education. Strong engineers investigate odd graphs and small anomalies before they become outages.
SRE skills that matter more in 2026, and less
Mattering more. Controlling telemetry cost has become an engineering task, since observability bills can rival what they measure. AI assistants now summarise incidents, correlate signals and suggest likely causes, so engineers must be able to test a suggestion instead of trusting it, and to set limits on any agent allowed to act on production. Many products now depend on model APIs, which brings new failure modes including rate limits, token budgets and quality that degrades without a single error. Dependency mapping has grown more important as well, because one provider’s outage can spread through many vendors at once. Reliability work has also moved earlier, into design reviews and safer configuration rollouts.
Mattering less. Hand-tuned static thresholds on individual hosts are fading, and so is babysitting named servers, now that most workloads are disposable. Operating your own metrics and logging clusters is often unnecessary at smaller scale, since managed services exist for that. Writing dashboards for every component adds little if nobody reads them. The manual drafting of timelines is also going away as assistants assemble them from chat and alerts, though a person still needs to check them.
Site reliability engineer skills matrix by level
| Skill area | Junior | Mid-level | Senior |
|---|---|---|---|
| SLOs | Reads and reports on SLOs | Defines SLOs for several services | Sets the error budget policy with leaders |
| Alerting | Tunes noisy alerts with guidance | Builds burn rate alerts | Sets paging standards across teams |
| Observability | Builds dashboards, reads traces | Instruments services, controls cardinality | Designs telemetry strategy and spend |
| Resilience | Knows common failure patterns | Adds timeouts, retries and fallbacks | Runs design reviews, plans regional failure |
| Capacity | Tracks usage trends | Load tests and forecasts a service | Plans peak events across the company |
| Coding | Small scripts and fixes | Ships tested internal tools | Designs safe automation frameworks |
| Incidents | Shadows, then supports | Primary on-call, leads smaller incidents | Commands major incidents, coaches others |
How to assess SRE skills in a hiring process
Separate reliability from delivery first, because the two are often blurred in a single advert. The site reliability engineer job description shows how to draw that line, and it covers first-round questions. These later stages rely on realistic artefacts.
- Bring a real graph. Show a sanitised latency and traffic chart from a past incident, with the cause hidden. Ask what they see and what they would query next. People who have lived with these graphs talk in terms of hypotheses.
- Do a design review together. Share a short proposal for a new service and ask what would page them at night. Their questions on dependencies, limits and rollback reveal how they think.
- Walk the reasoning behind an SLO they set. Ask what they chose, who pushed back and what changed afterwards. A target with no argument behind it was probably copied.
- Allow AI help in an exercise. Give an assistant’s suggested root cause alongside the evidence, with the suggestion slightly wrong. See whether they test it or accept it.
Red flags on a CV: SRE titles with no mention of SLOs, incidents or on-call; dashboards built, but no decision changed as a result; “99 point something” availability quoted with no explanation of how it was measured; and tool lists that look identical to a DevOps engineer’s.
Frequently asked questions
How deep should Kubernetes knowledge be for an SRE?
Deep enough to reason about scheduling, networking and resource limits under failure, if you run on it. Cluster internals matter more where you operate it yourself. On a managed service, understanding how workloads behave is the priority.
Can a DevOps engineer or systems administrator become an SRE?
Yes, with effort in the right areas. DevOps engineers usually need more practice with SLOs and incident leadership. Administrators bring excellent systems instincts, but often need stronger software skills. Check for evidence of either person already working in that direction.
Could AI agents take over on-call?
They can handle triage, gather context and sometimes run a known fix. Novel failures and judgement calls during a major incident still need a person, and someone must answer for what any automated system does in production. Treat them as a capable assistant on the rota.
Is the Google model of SRE realistic for a mid-sized company?
Parts of it are. Few companies can run a separate team with strict handover rules, and most embed SREs with product groups. Error budgets, blameless reviews and toil reduction all work at smaller scale, so copy the principles and leave out the organisation chart.
We screen on the real incidents, clusters and SLOs candidates have handled, so start a conversation with Zilo AI about the services you run and the rota you need to staff. To hire site reliability engineers in India, choose contract, contract-to-hire or permanent terms, and the IT hiring page lists related DevOps and cloud roles.