Site Reliability Engineer Skills: What Actually Separates an SRE From a DevOps Engineer

Your company just renamed the platform team “SRE.” The JD looks almost identical to the DevOps JD, so what actually changed? More importantly, which site reliability engineer skills are you missing?

Yes, SRE contributes a measurement discipline through SLIs, SLOs, and error budgets, along with an operational discipline around on-call, incident response, postmortems, and controlling toil. The tools overlap heavily with DevOps. The judgement does not.

This difference is important for you if you have been working with cloud, CI/CD, containers, or Kubernetes and need to choose what new skills to acquire. The focus on SRE practices is not about learning something new but about ensuring reliability.

What a Site Reliability Engineer Actually Does

SRE was defined at Google by Ben Treynor Sloss as “what happens when you ask a software engineer to design an operations function.” This idea explains the role quite perfectly. SRE views reliability as an engineering challenge, and thus focuses on minimizing the operational effort required by software automation.

Practically speaking, an SRE could dedicate the week to being responsible for the SLOs of a number of services, participate in an on-call schedule, assess the readiness of a service for going live, and write code to automate tedious operations tasks.

These are the core site reliability engineer roles and responsibilities: keeping services within their reliability targets, responding to incidents, reducing toil, and improving the systems behind them. 

One of the core components of the SRE’s role is the Production Readiness Review (PRR). Prior to assuming ownership of a service, the SRE team can determine if the service is dependable enough to be supported by the team. If a particular service fails to meet the SLOs, then the team can pass the pager back to the product team.

And this is why SRE differs from a typical DevOps role. DevOps is largely a culture and delivery approach. SRE is an engineering role with measurable reliability targets. What’s important is the SLO.

The majority of SREs usually have experience from infrastructure, operations, or DevOps. If you need to start with this first, refer to DevOps Roadmap 2026: How to Become a DevOps Engineer

The Error Budget: The One Idea That Makes SRE Different

An error budget is the amount of unreliability a service is allowed in a given window. It is simply 1 − SLO. A 99.9% availability target over 30 days therefore gives the team 0.1%, or about 43 minutes, of permitted failure.

It is important to understand the error budget in SRE because it connects reliability targets with everyday engineering decisions. 

The error budget also gives the team a way to decide what happens next. If the service is meeting its SLO and there is error budget left, planned releases can continue. Once that budget is used up, the team can put new releases on hold and work on the reliability problems first.

SLI vs SLO vs SLA

Term	What it is	Who it’s for	Example
SLI	A measured reliability indicator	Engineering	99.95% of valid requests succeed
SLO	The internal reliability target	Engineering + product	99.9% availability over 30 days
SLA	An external commitment with consequences	Customer + business	Service credits below 99.5%

A good SLI should be measured as close to the user as possible and expressed as a ratio of good events to valid events. CPU usage, for example, is a system metric but not a reliability objective. SRE teams generally set the SLO tighter than the SLA so they see problems before customers face contractual consequences.

A worked example: 99.9% over 30 days

For a 30-day window:

• 30 × 24 × 60 = 43,200 minutes
• 0.1% error budget = 43.2 minutes

Availability SLO	Error budget per 30 days	Error budget per year
99%	432 min (7 hr 12 min)	3 days 15.6 hr
99.5%	216 min (3 hr 36 min)	1 day 19.8 hr
99.9%	43.2 min	8 hr 45.6 min
99.95%	21.6 min	4 hr 22.8 min
99.99%	4.32 min	52.6 min
99.999%	25.9 sec	5.26 min

Now spend that budget across the month:

• Day 4: bad deploy. Checkout is down for 22 minutes. 21.2 minutes remain (49%).
• Day 12: partial degradation. 10% of requests fail for 90 minutes. That costs 90 × 0.10 = 9 minutes, leaving 12.2 minutes (28%). This is why graceful degradation can be preferable to a complete outage.
• Day 23: dependency timeout. A payment-gateway retry storm costs another 14 minutes. The budget reaches −1.8 minutes and is exhausted.

What happens when the budget is gone?

The team should already have an error budget policy agreed between product and engineering. When the budget has been exhausted, launching new features may halt, and the upcoming sprint should be devoted to work on improving reliability, like making improvements to the deployment pipeline, canary deployments, and circuit breakers. Riskier changes may require explicit approval.

The unutilized budget could also prove to be revealing. A 99.99% attainment for a service on a 99.9% SLO could imply excessive caution by the team.

Understanding Burn Rate in SRE

Burn rate is how quickly a service consumes its error budget. A burn rate of 1 means the budget will be exhausted exactly by the end of the SLO window.

Budget consumed	Over window	Burn rate	Action
2%	1 hour	14.4×	Page immediately
5%	6 hours	6×	Page
10%	1 day	3×	Ticket
10%	3 days	1×	Ticket

“CPU is above 80%” shows that the system is under pressure. “We will exhaust our error budget in four hours” shows that the reliability problem needs attention soon. The second alert gives the team a clear reason to investigate the problem before continuing with planned releases.

SLOs and error budgets help the team track reliability and decide when to fix a problem before releasing more changes.

11 Essential Site Reliability Engineer Skills

Site Reliability Engineering involves measuring reliability, handling production incidents, reducing repetitive operational work, and writing software that makes systems more dependable. You need a mix of software engineering, infrastructure, and operations knowledge to handle that work.

The SRE skills required can therefore include reliability measurement, incident response, automation, system design, monitoring, and programming.

Skill	Why an SRE needs it	How it differs from DevOps	How to evidence it
1. SLIs, SLOs & error budgets	They drive reliability decisions, alerts and release priorities.	DevOps monitors uptime; SRE sets targets and acts when they are missed.	Define an SLO, build a budget dashboard and write the policy.
2. Observability & monitoring	Helps diagnose failures that weren’t predicted.	DevOps builds monitoring; SRE decides what signals actually matter.	Improve alerting or add tracing to a request path.
3. Incident command & on-call	Production ownership requires coordinated response.	DevOps is often escalated to; SRE owns the response process.	Run an incident drill and document the timeline.
4. Postmortems & blameless culture	Failures should lead to permanent fixes.	DevOps may write an RCA; SRE makes postmortems part of the process.	Write a blameless postmortem with owned action items.
5. Capacity planning & load testing	Reliability depends on having enough capacity before demand arrives.	DevOps may configure autoscaling; SRE forecasts demand and tests limits.	Produce a load-test report and capacity recommendation.
6. Toil reduction & automation	Manual operational work grows with the service.	DevOps automates deployment; SRE measures and systematically reduces toil.	Replace a repetitive runbook task with automation and show time saved.
7. Distributed-systems reliability	Failures often emerge across multiple components.	DevOps configures infrastructure; SRE reasons about cascading failure and back-pressure.	Demonstrate backoff, circuit breaking, or load shedding.
8. Chaos engineering	Failure scenarios need to be tested, not assumed.	Less common in traditional DevOps work; SRE tests failure deliberately.	Run a game day and document what failed unexpectedly.
9. Coding ability	SREs build tools and services that reduce operational work.	DevOps often relies more on scripts and configuration; SREs have a stronger software-engineering bar.	Build a tested, documented project such as an operator or synthetic-monitoring service.
10. Linux internals & networking	Debugging eventually reaches the layer beneath the application.	DevOps knows the commands; SRE needs to understand the underlying mechanisms.	Document a root-cause investigation using tools such as strace, ss, or tcpdump.
11. Kubernetes & infrastructure as code	Reliability controls increasingly live in the platform.	The tools overlap heavily; SRE focuses more on their failure modes.	Build a Terraform module or Helm chart with rollback and a failure drill.

Incident Command and the Reality of On-Call Work

When you are on call, you need to know who is handling the alert, who takes over if they are unavailable, and when the issue needs to be escalated. A primary and secondary engineer, a clear escalation policy, and a proper handoff between shifts keep that process clear. For teams spread across India, Europe, and the US, a follow-the-sun model can also reduce the number of overnight pages handled by one location.

A serious incident can involve several people, but they should not all be trying to do the same thing. The Incident Commander coordinates the response, the Operations Lead handles production changes, and the Communications Lead keeps stakeholders and status pages updated. So if a major incident happens at 3 a.m., one person can coordinate the response while another handles the production changes and someone else keeps everyone informed.

The number of pages you receive during a shift also tells you something about the on-call setup. A commonly used rule of thumb is around two pages or fewer per shift. If engineers are being paged repeatedly, the team needs to look at the alerts and the systems behind them. Each page should point to something that needs action.

Before accepting an SRE role, ask:

1. How large is the on-call rotation?
2. How many pages does an engineer typically receive per shift?
3. Is on-call compensated?
4. Is time off provided after a particularly bad shift?
5. What percentage of recent pages were actionable?
6. Does the team have a written error-budget policy?

Pay particular attention to the error-budget question. If the team has an SRE title but does not use SLOs or an error budget when handling reliability or release decisions, the role may be more inclined towards DevOps than SRE.

Toil & the 50% rule

Toil is operational work that is manual, repetitive, automatable, tactical, has little lasting value, and grows with the size of the service. The Google SRE model treats it differently from normal engineering work because unchecked toil can consume an entire team’s capacity.

The goal is to keep the percentage of operational tasks below 50%, while the other 50% should be spent on building solutions that would minimize operations in the future. In case the SRE group consistently spends more than 50%, there must be an action taken; automation, hiring, system design, or even reassignment of the pager duty.

You can test it through these simple questions:

• Does the task keep recurring?
• Could a script or system handle it?
• Does it leave no lasting improvement?
• Does the workload increase as the service grows?

If the answer is yes to all four, you are probably looking at toil.

Not every manual task is toil, though. Design review, capacity forecasting, postmortem, and mentoring may take some time, but they create long-term value. It is important to note that while it is possible to automate everything in site reliability engineering, automation in this field is actually aimed at removing human effort from unnecessary tasks.

Chaos engineering and testing for failure

Chaos engineering begins with a steady state assumption; describe what normal behavior should be, cause a failure, and check whether the system behaves as predicted. The failure will have an identifiable blast radius, and the outcome should give rise to a solution or control.

Here, you can start small. On a personal cluster, that might mean killing a pod during a request, adding latency to a dependency, blocking DNS, filling a disk, or draining a node.

Start with game days before attempting continuous automated chaos. A quarterly failure exercise is still useful if it exposes a weakness the team did not know about.

How much coding does an SRE really need?

Indeed, there is quite a bit of coding involved in SRE. SREs build and maintain software, not just scripts and configuration. 

Some examples of what such software might include are a Kubernetes operator, internal tools for deployments, synthetic monitoring services, capacity modeling jobs, and chaos testing tools. What really matters is that the code should be tested, reviewed, versioned, and maintainable.

Regarding languages, Go is recommended for infrastructure tasks, whereas Python can serve as an excellent starting point for automation and broader tooling. Bash remains useful for smaller operational tasks.

And don’t assume SRE means skipping coding interviews. Product companies often test programming and data-structures fundamentals, even when the role places more emphasis on Linux, networking, and distributed systems than a typical SDE position.

Learn More With: Operating System Course with Certification

Reliability Patterns an SRE Is Expected to Reason About

A small failure can create a much bigger problem when other parts of the system react to it. A slow dependency can trigger retries; those retries can increase the load, and the extra load can leave other services without enough threads or connections. The patterns below deal with these situations.

When retries make the problem worse

A retry storm starts when a dependency slows down, and clients keep retrying their requests. If hundreds of clients retry at once, they add even more traffic to a service that is already struggling. Exponential backoff and jitter spread those retries over time, while a retry budget limits how much additional traffic retries can create.

Retries also need to be safe. Suppose a payment request succeeds, but the response times out before reaching the client. Retrying the request could charge the customer twice. Idempotency lets the system recognise that the operation has already been processed, usually through an idempotency key.

When one failure spreads to other services

A cascading failure can begin with one slow dependency. Upstream requests keep waiting until threads, connections, or other resources run out. Timeouts stop requests from waiting indefinitely, while circuit breakers stop calls to a failing dependency for a period and later allow limited traffic through to check whether it has recovered. Back pressure also helps control how much work a system accepts when a downstream service cannot keep up.

A thundering herd creates a different kind of pressure. Thousands of clients might reconnect after an outage or request the same data when a shared cache expires. Expiry jitter spreads cache refreshes over time, and request coalescing allows identical requests to share the same result instead of hitting the service separately.

Bulkheads protect shared resources when one workload starts consuming too much capacity. Threads, connections, or other resources can be separated between workloads so that one problem does not use everything available to the service.

When the system cannot handle all the demand

There are situations where the incoming traffic is simply greater than the system can handle. Load shedding allows the service to reject lower-priority requests, while graceful degradation keeps a reduced version of the service available. A recommendation feature, for example, can be unavailable while the main transaction still works.

All of these patterns deal with a system that is starting to struggle. They give the SRE different ways to keep that problem under control: retries can be slowed down, failing services can be temporarily cut off, shared resources can be protected, and lower-priority work can be dropped when the system is overloaded. An SRE needs to understand what is causing the pressure, what resources are being affected, and which mechanism can keep the problem from becoming a larger outage.

For the broader system-design concepts behind these patterns, see System Design Roadmap 2026: Complete Guide from Beginner to Advanced.

SRE vs DevOps Engineer: A Skills-Level Comparison

This table compares the two roles on skills and day-to-day work. For the fuller role comparison, including history, organisation structure, and which career to pick, you can check out this SRE vs DevOps guide.

Dimension	DevOps engineer	Site reliability engineer
Core mandate	Ship faster and more safely; reduce friction between development and operations	Keep services within an agreed reliability target through engineering
Unit of work	Pipeline, environment, deployment	Service SLO and error budget
How success is measured	Deployment frequency, lead time, change-failure rate, MTTR	SLO attainment, error-budget burn, toil and page volume
Release velocity	Accelerates it	Governs it and can pause it when reliability drops
Production ownership	Often shared or advisory	Explicit, including production-readiness decisions
On-call	Common, but often less formal	Defined rotation, incident roles and escalation policy
Coding expectation	Scripting, configuration, IaC and automation	Stronger software-engineering bar; builds and maintains internal services
Time allocation	Operational work varies by team	Ops work is capped at roughly 50%, with the rest spent on engineering
Typical artefacts	Pipelines, Terraform, Helm charts, runbooks	SLOs, error-budget policies, postmortems, reliability tooling
Interview focus	Tooling, cloud architecture, pipelines and troubleshooting	Coding, Linux/networking, distributed systems, SLOs and incident scenarios

DevOps and SRE share roughly 70–80% of their tooling. SLOs, error budgets, formal on-call responsibility, and software development for reliability are some of the areas that distinguish the SRE side of the work. If you are already in DevOps and your role starts including these responsibilities, you are doing SRE-type work even if your title still says DevOps.

If you want to build on your DevOps experience, read more at: DevOps career path.

Platform Engineering and SRE

While platform engineering creates the internal developer platform used by teams for deployment and management of their applications, site reliability engineering is concerned with the reliability of such applications and systems that support them. Platform engineering and site reliability engineering might be present in the same organization, yet their areas of focus are different.  

Platform engineers ask, “How can we make the paved road easier and safer for developers?” SREs ask, “Is the service reliable enough, and what happens when it isn’t?” Some companies combine parts of these responsibilities, while others keep the roles separate. Platform engineering usually focuses on the internal tools, infrastructure, and workflows developers use to build and deploy services. SRE focuses on the reliability of those services in production, including SLOs, incident response, and operational work.

Site Reliability Engineer Salary and Career Path in India

Site reliability engineer salary in India is one of the easiest parts of an SRE career to oversimplify. Because the thing is that the title alone may not always determine what your salary can be. Company type, experience, scope of ownership, and on-call responsibilities can make a substantial difference, so the figures below are all in accordance with current market benchmarks, and they can vary depending on various other factors.

Level	Experience	Median CTC (₹ LPA)
SRE I / Associate SRE	0 – 1 yrs	₹7 – ₹14 LPA
SRE II	2 – 5 yrs	₹12 – ₹25 LPA
Senior SRE	5 – 8 yrs	₹18 – ₹32 LPA
Staff / Lead SRE	8+ yrs	₹25 – ₹46 LPA

*The salary ranges have been taken from Glassdoor and can even vary depending on location, company type, and experience.

Also Read: DevOps Engineer Salaries in India: How Much Can You Really Earn?

How the career actually changes with seniority

Level	What changes
SRE I	Supports existing services, follows runbooks, handles incidents, and works within established SLOs.
SRE II	Owns services more independently, improves monitoring and reliability, and starts influencing SLOs and operational practices.
Senior SRE	Owns reliability across multiple services, leads major incidents, designs reliability improvements and mentors other engineers.
Staff / Lead SRE	Works across teams and systems, sets reliability strategy, influences architecture and helps define organisation-wide standards.

SRE and DevOps salaries can overlap heavily in India. Where an SRE premium exists, it is more likely to reflect the company, engineering scope and on-call compensation than the job title itself.

You can see most SRE hiring in Bengaluru, Hyderabad, Pune, Chennai and Gurugram, particularly at global product companies, GCCs, Indian product companies, fintechs and large technology platforms. As you move into senior roles, you can also expect more ownership of reliability targets, engineering standards and system design, rather than only operating systems that someone else has designed.

How to Move From DevOps (or Backend, or Sysadmin) Into SRE

The move into SRE depends on where you are starting. You do not need to relearn everything. You need to identify the part of the SRE job your current role has not exposed you to yet.

Coming from DevOps: You probably already know cloud, CI/CD, infrastructure, and automation. Your bigger gaps are measurement, formal incident practice, and software engineering. Start by taking a service you already operate, defining an SLO for it, and writing down what should happen when its error budget is exhausted. 

Coming from backend engineering: Your coding experience will help, but you’ll also be working with software after it goes into production. That means monitoring services, responding to incidents, investigating failures, and making changes that keep the system reliable. You’ll work with Linux and networking, take part in the on-call rotation, and troubleshoot production issues when they happen. A service can go down at midnight, and you may be the person investigating what happened and getting it back up.

Coming from sysadmin or support: You already have operational instincts and troubleshooting experience. The missing piece is usually software engineering. Take a recurring runbook task and turn it into a maintained tool or service with tests, version control, and documentation.

A practical 90-day SRE roadmap

You can use the 90 days to move from understanding SRE concepts to actually working with them. The first few weeks focus on reliability measurement, then you get into monitoring and automation, and the final weeks give you a chance to test what you have built.

Weeks 1 - 2: Start with SLOs: Read the SLO chapters of the Google SRE Workbook and pick a service you can measure. Write one SLI and one SLO for it. If you are coming from DevOps and want to strengthen that foundation first, you can check this DevOps Course Syllabus 2026 to see where these two areas may overlap.

Weeks 3 - 4: Add monitoring: Use Prometheus and Grafana to instrument the service. Create a dashboard and add a burn-rate alert so you can see how quickly the service is using its error budget.

Weeks 5 - 6: Automate something repetitive: Keep track of your operational work for two weeks. Find the task you repeat most often, automate it, and record how much time the automation saves. This gives you a small but concrete example of the kind of engineering work SRE involves.

Weeks 7 - 8: Work through an incident: Shadow an on-call rotation if you have access to one. Then write a blameless postmortem for an incident, with clear action items for preventing or reducing the chance of the same problem happening again.

Weeks 9 - 10: Build something: Write and ship a small Go or Python project such as a probe service, SLO reporter, or operator. Add tests and a README so someone else can understand and run it.

Weeks 11 - 12: Test the system: Run a small game day. Decide what you expect the system to do, introduce a controlled failure, and record what actually happens. Compare the result with your expectations and document anything that needs to change.

If you are considering SRE as an extension of your DevOps career, Is DevOps a Good Career? can help you compare the career paths. And if you are looking at upskilling as part of a career move, the Scaler Career Transition Assessment Report has data on salary outcomes after upskilling.

Do you need an SRE certification?

There is no single SRE certification that employers universally expect. Certifications such as CKA, CKAD, and cloud certifications can demonstrate knowledge of Kubernetes or a particular cloud platform, but they cover only part of the work involved in SRE. SLOs, incident management, and reducing operational work are usually learned through working with production systems and SRE practices.

If a job specifically asks for a certification, include it in your preparation. Otherwise, your projects and experience can give interviewers something to discuss, such as an SLO dashboard, a postmortem, an automation project, or software you have built and maintained.

Site Reliability Engineer Interview Questions and What They're Testing

SRE interview questions can cover tools, but you will also be asked to work through production failures, explain your reasoning, and write or review code. You may have to decide what to investigate first during an incident, explain why you chose a particular approach, or work through a reliability problem where there is no obvious answer.

Round	Sample question	What they're actually testing
Coding	“Parse these logs and report the p99 latency per endpoint.”	Whether you can write clear, maintainable code rather than just produce a working one-liner.
Linux / systems	“A process is using 100% CPU and the box is unresponsive. Walk me through it.”	Whether you understand top, strace, perf, load average, runnable processes, and the OOM killer.
Networking	“The site is slow from Mumbai but fast from Bengaluru. How would you debug it?”	DNS, routing, TLS, CDN behaviour, mtr and tcpdump.
Distributed systems	“A payment gateway your service depends on is suddenly 10× slower. What happens?”	Retry storms, timeouts, circuit breakers and load shedding.
SLO scenario	“You've burned 80% of your error budget on day 10. What now?”	Whether you understand burn rate, error-budget policy, and when risky releases should pause.
Incident / behavioural	“Tell me about an outage you handled.”	Whether you can explain detect → triage → mitigate → resolve → postmortem without blaming individuals.

An interviewer may ask about an SLO during a production incident. You should be able to explain the burn rate, how much error budget remains, and whether the team should continue with a planned release or focus on reliability work first.

Pick one incident and work through it from the first alert to the final fix. Include the numbers: how long it took to detect, how long it took to mitigate, and how much error budget was consumed. Then explain what you checked, what you changed, and what happened afterward.

Common Mistakes When Moving Into SRE

Moving into SRE is not just a matter of learning a few more tools. The bigger mistakes usually come from misunderstanding what the role is actually responsible for.

• Treating SRE as “DevOps with a better title.” If you cannot define an SLO for the service you own, you are probably still approaching reliability as a tooling problem. Fix: start with a measurable SLO and use it to guide reliability decisions.
• Setting the SLO at 100% or copying “five nines.” Perfect reliability sounds good but can be unnecessarily expensive, while a copied target may not reflect what users actually notice. Fix: choose a reliability target based on user experience and the cost of achieving it.
• Alerting on causes instead of symptoms. Pages for CPU or memory thresholds can quickly become background noise that everyone ignores. Fix: alert on user-facing symptoms and error-budget burn when they require action.
• Repeating the same manual fix. In case you find yourself fixing the same failure every week manually, you are adding to operational debt and not addressing the issue. Fix: treat recurring manual work as toil and automate or redesign it.
• Writing blame-focused postmortems. Naming the person who made the mistake discourages people from reporting failures honestly. Fix: focus on the conditions that allowed the failure and assign concrete corrective actions.
• Regardless of the 50% cap on operations. The team spending 90% of its time on incidents and requests will have no bandwidth left to enhance reliability. Fix: measure toil and reserve engineering time to eliminate its causes.
• Joining without understanding the on-call model. A team with no error-budget policy or clear approach to on-call compensation can turn a good role into a burnout cycle. Fix: ask about SLOs, page volume, escalation, compensation, and recovery time before accepting the role.

Is SRE a Good Career in 2026? Can AI Replace It?

AI has already transformed the tasks of an SRE but only through automation of the tedious aspects and not through delegation. AI can assist in anomaly detection, generation of summaries for log files, drafting a postmortem, suggesting actions from runbooks, and associating an alert with a deployment that just happened. Those capabilities can save an SRE a lot of time.

But amidst all the services that AI can possibly provide, a human decision is still needed. Someone has to negotiate an SLO with the product team, decide whether an error-budget policy should trigger a feature freeze, take charge during a major incident, and redesign a system so the same failure does not keep happening.

That actually fits the SRE model well. AIOps can alleviate toil, and reducing toil is exactly what SRE teams are supposed to do. With the reduction of repetitive operations through automation, engineers will have enough time to work on reliability engineering, architecture, and improving the systems they own.

So, can AI replace SREs? It is more likely to change the job than eliminate it. Engineers who perform routine monitoring, ticketing, and troubleshooting are the SREs that will be more vulnerable due to automation. Those engineers who think through distributed systems, reliability goals, failure modes, and engineering tradeoffs in production are still very hard to automate.

As systems become more complex, reliability work does not disappear simply because the tooling gets better. The stronger career bet is therefore not to compete with automation on repetitive tasks, but to move toward the engineering and decision-making side of SRE.

Conclusion

Now that you’ve had such a detailed read, you might have understood how much judgement SRE actually involves.

You have to decide how reliable a service needs to be, which problems need attention first, when a release should wait, and where automation is worth the effort. You will make those calls using the information available from the service, the users it serves, and what you are seeing in production.

That judgement develops as you work through different systems and situations. There will be incidents where the right response is obvious, and others where you have to weigh reliability, engineering effort, product requirements, and the risk of leaving a problem unresolved.

Want to build the DevOps foundation behind SRE?

If you want to move towards SRE, a strong understanding of DevOps, cloud infrastructure, automation, and deployment practices gives you a solid base to work from. Scaler’s DevOps Course covers these areas through a structured curriculum and hands-on projects.

FAQ

1. What skills does a site reliability engineer need?

The eleven core skills are: SLI/SLOs and error budgets, observability, incident response, blameless postmortems, capacity planning, toil reduction, distributed systems reliability, chaos engineering, software engineering, Linux and networking, and Kubernetes with infrastructure as code.

2. What is an error budget in SRE?

An error budget is the amount of unreliability a service is allowed within a given window, equal to 1 − SLO. A 99.9% availability SLO over 30 days allows about 43 minutes of failure. When the budget runs out, an agreed policy may pause or restrict feature launches until reliability work restores it.

3. What are the four golden signals of SRE? Latency, traffic, errors, and saturation.

Latency, traffic, errors, and saturation are the four signals recommended for monitoring user-facing systems. Latency shows response time, traffic measures demand, errors track failed requests, and saturation shows how close a resource is to its capacity. They are often referred to as the “four golden rules,” but the correct term is golden signals. 

4. Does SRE involve coding?

Yes, SRE involves substantial coding. SREs are expected to build and maintain real software, such as operators, probes, SLO reporters, and automation services, not just scripts. SRE interview loops often include a coding round, and Go and Python are common languages for SRE work.

5. What is the difference between an SRE and a DevOps engineer? 

The tools overlap, but SRE adds formal reliability measurement through SLOs and error budgets, production ownership, structured on-call and incident response, and a stronger software-engineering focus.

6. What is toil in SRE, and what is the 50% rule?

Toil is repetitive, manual, and automatable operational work with little lasting value. SRE teams aim to keep it below 50% of their time and use the remaining time for engineering improvements.

7. Is SRE a good career in India?

Yes, especially for engineers who enjoy production systems and debugging. Hiring is concentrated in major tech hubs like Bengaluru, Hyderabad, Pune, Chennai and Gurugram, with salaries varying by experience, company and role. 

8. Can AI replace site reliability engineers?

AI can automate parts of SRE work, including anomaly detection, log analysis and postmortem drafts. But SLO decisions, incident command and designing systems to prevent recurring failures still require engineering judgement.