Cloud Computing Explained for Recruiters: AWS, Azure, GCP

If you're a technical recruiter hiring software engineers, you've likely noticed cloud computing appearing in nearly every job description. Candidates talk about their EC2 experience, their Azure deployments, their Kubernetes clusters. But if your background isn't in engineering, these terms can feel like alphabet soup.

The good news: you don't need to be a cloud architect to hire cloud engineers effectively. What you do need is a working understanding of the major platforms, what skills matter, and what salary expectations are realistic.

This guide breaks down cloud computing in plain language designed for recruiters. We'll cover the three market leaders—AWS, Azure, and GCP—and explain why they matter for your hiring strategy.

What Is Cloud Computing? (And Why Recruiters Should Care)

Cloud computing is the delivery of computing power, storage, and applications over the internet instead of local machines or on-premises servers. Instead of companies buying and maintaining their own data centers, they rent computing resources from cloud providers.

For recruiters, this matters because:

Massive hiring demand: Companies are migrating to the cloud at scale, creating steady demand for cloud-skilled engineers.
Salary premiums: Cloud engineers command higher salaries than general software developers (typically 10-20% more).
Niche specialization: Cloud skills are less commoditized than basic web development, giving you leverage in placements.
Predictable skill requirements: Major cloud platforms have clear, certifiable competencies you can validate.

The global cloud computing market is projected to exceed $600 billion by 2025, according to market research firms. That translates to constant demand for cloud architects, DevOps engineers, and cloud-native developers.

Platform	Market Share	Strength	Best For
AWS	~32%	Broadest service catalog, most mature ecosystem	Enterprise, startups, all company sizes
Azure	~23%	Enterprise integration, hybrid cloud	Fortune 500, Microsoft shops
GCP	~11%	Data analytics, machine learning, developer experience	Data-heavy companies, AI/ML startups

AWS (Amazon Web Services)

AWS is the market leader and has been since 2006. If you're hiring cloud engineers, the majority will have AWS experience.

Key services recruiters see on résumés: - EC2 (virtual machines/servers) - S3 (object storage) - RDS (managed databases) - Lambda (serverless computing) - VPC (networking)

AWS dominates because it has the broadest range of services (200+ available), the most mature ecosystem, and the largest community. This also means AWS credentials are the most common—certifications like the AWS Solutions Architect Associate are industry standard.

What to expect in salary ranges: - AWS Developer: $100K–$140K - AWS Solutions Architect: $140K–$180K - AWS DevOps Engineer: $120K–$160K

(Varies by location, experience, and company size.)

Microsoft Azure

Azure is positioned as the enterprise and hybrid cloud solution. Many Fortune 500 companies run Windows Server or SQL Server workloads, and Azure integrates seamlessly with the Microsoft ecosystem.

Key services you'll see: - Virtual Machines - App Service (managed application hosting) - SQL Database and Cosmos DB - Azure DevOps - Logic Apps - Azure Kubernetes Service (AKS)

Azure has grown aggressively over the past 5 years and now rivals AWS in some enterprise segments. If a company already uses Microsoft products (Office 365, Active Directory, Dynamics), they're more likely to adopt Azure.

Salary expectations: - Azure Developer: $105K–$145K - Azure Solutions Architect: $145K–$190K - Azure DevOps Engineer: $125K–$170K

Azure roles typically pay slightly more than AWS equivalents, reflecting the enterprise premium.

Google Cloud Platform (GCP)

GCP is the data and AI specialist. Google built GCP on its own infrastructure (the same systems running Google Search and YouTube), and it shows in data analytics capabilities.

Key services: - Compute Engine (VMs) - Cloud Functions (serverless) - BigQuery (data warehouse) - Cloud ML (machine learning) - Cloud Storage - Firestore (NoSQL database)

GCP appeals to startups and data-driven companies like Spotify, Snap, and PayPal. If a company is heavily invested in machine learning or big data, they're more likely to use GCP.

Salary expectations: - GCP Data Engineer: $110K–$160K - GCP ML Engineer: $130K–$180K - GCP Cloud Architect: $140K–$190K

GCP salaries are competitive, often at the higher end because data and ML roles command premiums.

Core Cloud Concepts You Need to Know

IaaS, PaaS, and SaaS

Recruiters need to understand the cloud service models because they map to different roles and skill requirements.

IaaS (Infrastructure as a Service) - You rent servers, storage, and networking - You manage applications, data, and middleware - Example: AWS EC2, Azure Virtual Machines - Engineer type: DevOps, Systems Engineers, Infrastructure Engineers

PaaS (Platform as a Service) - Cloud provider manages infrastructure AND middleware - You just deploy and manage applications - Example: AWS App Runner, Azure App Service, Heroku - Engineer type: Application Developers, Backend Engineers

SaaS (Software as a Service) - Fully managed, ready-to-use applications over the internet - Example: Salesforce, Office 365, Slack - Engineer type: Integration specialists, not typically "cloud engineers"

When screening candidates, ask which model they've worked with. An engineer who's built infrastructure on EC2 has different skills than someone who's deployed applications on App Service.

Containerization and Kubernetes

Containers (especially Docker) have become essential to cloud deployments. A container packages an application and all dependencies into a portable unit.

Kubernetes orchestrates containers at scale—managing deployment, scaling, and updates.

Why this matters for recruiting: - Kubernetes is a highly specialized skill that commands premium salaries ($150K–$220K+) - Many cloud roles now require Docker/Kubernetes experience - Candidates who know Kubernetes are immediately more marketable

Look for "Docker," "Kubernetes," and "container orchestration" on résumés. These signal senior-level cloud thinking.

Serverless Computing

Serverless platforms (AWS Lambda, Google Cloud Functions, Azure Functions) let developers write code without managing servers.

Pay only for computation used (not reserved capacity)
Auto-scales instantly
Ideal for event-driven workloads

Serverless is increasingly popular, and candidates with Lambda/Cloud Functions experience are in demand.

Which Cloud Skills Are Most In-Demand?

Based on job posting data and recruiter demand:

AWS certifications (Solutions Architect Associate, Developer Associate) — Most common, highest volume
Kubernetes — Highest salary premium, hot skill
DevOps pipeline experience (CI/CD, automation) — Universal across all clouds
Terraform/Infrastructure as Code — Growing demand, cloud-agnostic skill
Python for cloud/automation — Core skill across all platforms
Cloud security and compliance — Increasingly critical, especially for regulated industries

If you're sourcing for general cloud roles, AWS + Python + DevOps is the golden combination. If you're hiring specialists, focus on the platform your client uses.

Multi-Cloud and Hybrid Cloud Trends

Most enterprises now use multiple clouds. They might run core systems on Azure (legacy Microsoft integration) while using AWS for new microservices and GCP for analytics.

This creates opportunities for multi-cloud engineers—candidates who can work across platforms. They're valuable but less common.

Red flag for candidates: Claiming deep expertise in all three clouds equally. Real engineers have depth in one or two, with general knowledge of others.

Certification Landscape

Cloud certifications are highly valuable for hiring because they're vendor-validated and relatively standardized.

AWS Certifications (Most Common)

Cloud Practitioner (entry-level)
Solutions Architect Associate (mid-level, ~2 years experience)
Solutions Architect Professional (advanced)
Developer Associate (application development focus)
DevOps Engineer Professional (infrastructure and automation)

Certification holders typically earn 10-15% more than non-certified peers. If you see "Solutions Architect Associate" or "Developer Associate" on a résumé, it's a strong signal.

Azure Certifications

Azure Fundamentals (entry-level)
Azure Administrator Associate
Azure Solutions Architect Expert
Azure Developer Associate

GCP Certifications

Cloud Digital Leader (entry-level)
Associate Cloud Engineer
Professional Cloud Architect
Professional Data Engineer

Practical recruiting tip: Certifications are valuable but not everything. A candidate without certification who's built production systems is often stronger than someone with certs but no real-world experience. Use certifications as a filter, not a disqualifier.

Understanding Common Cloud Job Titles

Cloud titles can be confusing because they vary by company. Here's what you're actually hiring:

Title	Focus	Key Skills
Cloud Engineer / Cloud Developer	Building applications	Language proficiency, APIs, databases, microservices
DevOps Engineer	Infrastructure automation, CI/CD	Terraform, Docker, Kubernetes, scripting, monitoring
Cloud Architect	System design and strategy	All platforms, trade-offs, cost optimization
SRE (Site Reliability Engineer)	Reliability, monitoring, incident response	Observability, automation, database management
Cloud Security Engineer	Security and compliance	Identity management, encryption, audit, regulations
Data Engineer (Cloud)	Data pipelines and warehousing	SQL, Python, ETL, BigQuery/Redshift/Synapse

A "Cloud Engineer" at a startup might do DevOps work. The same title at a large enterprise might mean pure application development. Always dig into the specific responsibilities.

Salary Benchmarks by Role and Platform (2025)

These are approximate ranges for the US market (varies by location, company stage, and seniority):

Role	AWS	Azure	GCP	Notes
Cloud Developer	$100–$140K	$105–$145K	$105–$145K	Entry to mid-level
DevOps Engineer	$120–$160K	$125–$170K	$120–$165K	High demand
Cloud Architect	$140–$190K	$145–$200K	$145–$200K	Requires 5+ years experience
SRE	$130–$180K	$135–$185K	$135–$190K	Niche, senior role
Data Engineer	$110–$160K	$115–$165K	$120–$180K	GCP pays premium for ML/AI
Cloud Security	$130–$180K	$140–$195K	$130–$185K	Growing demand

Location matters significantly. A cloud engineer in San Francisco costs 40-50% more than the same role in Austin or Denver. Adjust expectations accordingly.

How to Talk to Cloud Candidates (And What to Ask)

Red Flags to Listen For

"I'm certified but I've never actually deployed to [platform]" — Certification without experience
"I've only used managed services like App Service/Elastic Beanstalk" — May lack infrastructure depth
"I don't know Kubernetes" — Fine for some roles, but increasingly limiting
"AWS is basically the same as Azure" — Shows surface-level understanding

Green Lights (What You Want to Hear)

"I've automated deployments using Terraform and built CI/CD pipelines" — Real DevOps experience
"I designed a microservices architecture using containers and orchestration" — Advanced thinking
"I've optimized cloud costs and reduced our AWS bill by 30%" — Business impact, not just technical
"I've worked across [Platform A] and [Platform B]" — Multi-cloud experience
Specific project stories with metrics and outcomes — Concrete experience, not buzzwords

Questions to Ask

"Walk me through a cloud architecture you've built or modified." — Listen for depth of understanding, not just tool names.
"How do you handle deployment and automation?" — Should mention CI/CD, Infrastructure as Code, at minimum.
"Tell me about a time you had to debug a production issue in the cloud." — Real-world problem-solving.
"How do you approach cost optimization?" — Shows maturity. Cloud bills spiral fast if not managed.
"Why did you choose [platform] for that project?" — Signals thoughtful decision-making, not just platform favoritism.

Using GitHub and GitHub Activity for Cloud Hiring

This is where Zumo comes in handy. You can analyze a candidate's GitHub activity to assess cloud skills:

Language usage: Python, Go, and Java are common for cloud infrastructure and backend systems. These signal cloud-ready developers.
Repositories and projects: Look for infrastructure-as-code (Terraform, CloudFormation), Docker files, Kubernetes manifests, or cloud SDK usage.
Contribution patterns: Consistent commits show hands-on experience, not just certification holding.
Documentation: Good README files and documentation signal professional approach to cloud infrastructure.

Zumo analyzes these signals to help you identify cloud-skilled developers based on actual work, not just what they claim on LinkedIn.

Regional Differences in Cloud Hiring

Cloud hiring varies by geography:

San Francisco / Bay Area: Heavy AWS and GCP (startup culture). Salary expectations highest.
Seattle / Pacific Northwest: Strong Azure presence (Microsoft). AWS strong here too.
New York / East Coast: Azure dominance (enterprise/finance). AWS competitive.
Austin / Denver: Growing cloud hubs, competitive on salary but slightly lower than coastal cities.
Remote candidates: Can command nearshore rates (20-30% lower) but expect higher salaries from cloud specialists.

Action Items for Your Recruiting

Build a platform preference map: What cloud does each of your major clients use? Focus sourcing energy there.
Get certified yourself (Cloud Practitioner level): Takes 20-30 hours of study. Helps you understand what candidates are learning and talk credibly about roles.
Create cloud role templates: Document the specific skills, certifications, and experience levels for Cloud Developer, DevOps, Architect roles for each platform.
Track certifications and skills: Build a tagging system in your ATS for cloud platform certifications and specializations.
Benchmark against market: Set salary expectations based on platform, location, and role type. Cloud roles are specialized—pay accordingly.
Use GitHub activity: Beyond résumés and interviews, evaluate actual coding work. This filters out over-certified but under-skilled candidates.

Conclusion

Cloud computing isn't going anywhere. AWS, Azure, and GCP command the market, and the demand for cloud-skilled engineers continues to grow. By understanding the basics—the platforms, the roles, the skill requirements, and the market rates—you position yourself to source and place cloud engineers more effectively.

The key is this: you don't need to be a cloud expert, but you do need to speak the language. Know the difference between IaaS and PaaS. Understand why Kubernetes matters. Recognize that AWS has broader adoption, Azure has enterprise strength, and GCP excels at data.

And when you're evaluating candidates, look beyond certifications to actual project experience. Cloud skills are best validated through work you can see—and that's exactly what GitHub activity reveals.

Frequently Asked Questions

What cloud skill should I focus on first when sourcing?

Start with AWS if you're building a cloud practice from scratch. It has the largest talent pool and market demand. Layer in Azure skills if you're working with enterprise/Fortune 500 clients, and GCP if you're sourcing for data-heavy or AI/ML companies.

How much does a cloud certification really matter?

Certifications are valuable as a filter—they prove a baseline knowledge level and commitment to learning. However, a candidate with 3 years of production cloud experience but no certification is often stronger than someone with 2 certifications but no real projects. Use certs as a positive signal, not a requirement.

Can a candidate be strong in multiple clouds?

Yes, but they'll have depth in one or two. True multi-cloud architects are rare and command premium salaries ($180K–$250K+). Most candidates specialize in one platform and have intermediate knowledge of others. That's normal and fine.

What's the salary difference between cloud engineers and regular software developers?

Cloud engineers typically earn 10-20% more than general software developers. A mid-level backend developer might make $120K; a cloud/DevOps engineer at the same level would be $130–$145K. Specialized roles (Cloud Architect, SRE, Security) command 20-40% premiums.

How do I verify someone actually knows their cloud platform?

Ask them to explain a specific project: architecture decisions, trade-offs they made, what they'd do differently. Someone who can talk through real production scenarios (including what went wrong) has genuine experience. Surface-level knowledge falls apart quickly in detailed discussion.

Find Cloud Engineers Faster With Zumo

Sourcing cloud engineers shouldn't require a tech degree. Zumo analyzes GitHub activity to identify developers with real cloud infrastructure experience—Python, Terraform, Kubernetes, the languages and tools that matter.

Skip the noise of over-certified but under-skilled candidates. Hire engineers based on the work they actually do.

Start sourcing cloud engineers on Zumo today.