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What AWS, Google Cloud, and Azure Actually Build — And Why It's a Career Your Kid Should Know About
Amazon Web Services runs 33% of the internet's computing infrastructure. The engineers who build and operate hyperscale cloud systems are among the highest-paid in existence — and the skills that get you there start earlier than most parents realize.
Amazon Web Services runs 33% of the internet. Not 33% of websites — 33% of the actual computing infrastructure the internet runs on, including Netflix, Zoom, Airbnb, and the backend of thousands of applications your family uses daily. Building and operating that infrastructure is one of the most technically demanding engineering jobs in existence, and the people who do it are among the highest-paid engineers on Earth. Most parents, when they think about engineering careers in tech, picture software developers or AI researchers. The people who build the infrastructure everything else runs on are largely invisible — and they wouldn’t have it any other way.
What “Cloud” Actually Is
The word “cloud” implies something ephemeral and weightless. The physical reality is the opposite. AWS, Azure, and Google Cloud are built on concrete, steel, and silicon spread across thousands of acres of physical data centers on six continents.
AWS has data centers in over 30 geographic regions, each region containing multiple “availability zones” — physically separate facilities within a metropolitan area, typically 10–100 km apart. Each availability zone is a cluster of data centers, each data center containing tens of thousands of servers. AWS operates millions of physical servers globally.
The engineering challenge is building systems across this physical infrastructure that behave as if they’re a single coherent computer — hiding the complexity, providing consistent performance, failing gracefully when components break (and components always break at this scale), and scaling to meet demand that can change by orders of magnitude within minutes.
When Netflix starts a blockbuster movie premiere and simultaneous viewership spikes to 10 million streams, AWS infrastructure responds automatically — provisioning compute, scaling networking, distributing the load. The engineers who built that response mechanism designed it years before it ran. The systems have to work correctly the first time, at full scale, under conditions that can’t be fully tested before they occur.
The Engineering Disciplines of Hyperscale Cloud
Distributed Systems Engineering is the foundational discipline of hyperscale cloud. How do you build a system across thousands of machines that behaves reliably? What happens when a network partition separates parts of the system? How do you ensure that data written to one part of the system is visible in other parts, and how quickly? These questions — formalized as consistency, availability, and partition tolerance in the CAP theorem — are the central intellectual problems of distributed systems engineering. Google’s Spanner, Amazon’s DynamoDB, and Google’s BigTable are landmark distributed systems that required years of engineering to develop and that are studied in graduate programs worldwide.
Site Reliability Engineering (SRE) was invented at Google to solve the operational problem: how do you keep millions of servers running reliably with a finite engineering team? SRE treats operations as a software engineering problem — instead of manually responding to every failure, you build automated systems that detect, diagnose, and remediate problems. Google’s original SRE book, published in 2016, has become a foundational text for the entire industry. SREs at major cloud providers earn $175,000–$250,000 at mid-career.
Network Engineering at hyperscale is an entirely different discipline from enterprise networking. AWS, Azure, and Google all operate their own global fiber networks, their own content delivery networks, their own custom networking hardware, and their own networking protocols developed specifically because standard internet protocols don’t meet their performance requirements. Google developed QUIC — which became HTTP/3 — partly to solve performance problems in their internal networking. AWS developed Nitro, a custom hypervisor and networking stack, to eliminate the overhead of conventional virtualization.
Storage Systems Engineering builds the systems that store the world’s data. AWS S3 — Amazon Simple Storage Service — stores hundreds of trillions of objects, ranging from the profile photo on your social media account to the backup of a hospital’s entire patient record system. Designing a storage system that is simultaneously extremely durable (eleven 9s of durability — 99.999999999%), available at high throughput, cost-efficient across hot and cold storage tiers, and operable at this scale requires engineering that pushes fundamental limits.
Security Engineering at cloud scale is a field unto itself. AWS, Azure, and Google are targets for attacks at a scale that no enterprise security team faces. They must protect not just their own systems but every customer’s workload running on their infrastructure. The shared responsibility model — where the cloud provider secures the infrastructure and the customer secures what they build on it — requires both technical security engineering and complex policy design.
The Research Picture
Gartner’s quarterly cloud market share analysis consistently confirms the Big Three’s dominance: AWS at approximately 31–33% of the global market, Azure at approximately 22–25%, and Google Cloud at approximately 10–12%. The combined market is growing at approximately 20% annually, meaning each percentage point of market share represents an enormous and growing revenue stream.
McKinsey’s Technology Council has identified cloud infrastructure engineering as one of the most talent-constrained specializations in technology. Their analysis finds that demand for distributed systems engineers and SREs consistently outpaces supply, with companies reporting that qualified candidates receive multiple competing offers within days of beginning interviews.
The Linux Foundation’s annual open source jobs report consistently finds that cloud infrastructure skills — particularly Kubernetes (the container orchestration system that Google open-sourced and that now runs most cloud workloads), cloud-native development, and DevOps/SRE practices — are the most in-demand skillsets in the industry.
Compensation data is at the extreme end of the software engineering spectrum. Data from levels.fyi, which crowdsources compensation data from verified professionals, shows that senior distributed systems engineers at AWS, Google, and Microsoft earn total compensation packages (base salary + bonus + equity) ranging from $350,000 to $600,000+ at the most senior levels. Mid-career engineers with 5–8 years of experience in cloud infrastructure earn $200,000–$350,000.
| Role | Mid-Career Total Comp | Skills Required |
|---|---|---|
| Distributed Systems Engineer | $200K–$350K | C++/Go/Java, consensus protocols, databases |
| Site Reliability Engineer | $180K–$300K | Python/Go, Linux, monitoring, on-call response |
| Cloud Network Engineer | $185K–$290K | Networking protocols, Linux networking, SDN |
| Storage Systems Engineer | $195K–$320K | C++/Go, distributed systems, hardware interfaces |
| Cloud Security Engineer | $190K–$310K | Cryptography, networking, identity systems |
Sources: levels.fyi, Glassdoor, LinkedIn Salary, company-specific data 2024–2025
The engineering culture at the cloud providers is also notable. AWS operates on a “two-pizza teams” model (teams small enough to be fed by two pizzas) with extreme ownership of services. Google’s SRE model is practiced industry-wide. Microsoft Azure has adopted DevOps practices that have influenced enterprise engineering globally. These companies don’t just employ great engineers — they develop them.
What This Means for Your Kid
Cloud infrastructure engineering is a career that’s built on depth. Breadth is useful — these engineers understand networking, storage, security, software systems, and hardware all at some level. But the ceiling in this career is set by how deeply you understand the fundamental problems.
The foundational subjects that matter:
Computer science fundamentals: Data structures, algorithms, and operating systems aren’t just interview topics — they’re the daily working material of distributed systems engineers. A student who understands why a hash table is the right data structure for a lookup operation, and what the tradeoffs are between different consistency models in a distributed database, has the foundation the field requires.
Networking: TCP/IP, DNS, routing protocols, and load balancing are the plumbing of cloud infrastructure. Every cloud engineer needs to be able to think clearly about how data moves across networks, where it can fail, and how to diagnose problems. This is genuinely learnable from first principles.
Linux operating systems: AWS, Azure, and Google Cloud run primarily on Linux. Engineers who are comfortable at the Linux command line — who understand processes, file systems, networking, and system calls — have the substrate knowledge for infrastructure work.
Programming: Go, Python, Java, and C++ are the dominant languages of cloud infrastructure. Go in particular has emerged as the systems language of cloud-native development (Kubernetes is written in Go, Docker is written in Go). Python is the scripting and automation language. C++ remains important for performance-critical systems code.
For parents, the practical observation is that kids who build things online — who run their own servers, who build web applications and want to understand how to deploy them, who learn how Linux works — are developing the precise intuition that cloud engineering requires. A teenager who has set up a home server, configured DNS, and deployed a web application has done more practical education for this career than any amount of general programming coursework provides.
This connects to the broader energy infrastructure picture we’ve covered in our piece on datacenter cooling engineering — the engineering challenges of hyperscale cloud extend from the software layer all the way down to how the buildings that house the servers are cooled and powered.
What to Watch Over the Next 3 Months
- AWS, Azure, and Google Cloud quarterly earnings. Revenue growth figures reveal which services are driving adoption — and therefore where engineering investment (and hiring) is concentrated
- Kubernetes project releases. Kubernetes is the defining technology of cloud-native infrastructure. Its release cycles and major features reveal where cloud infrastructure is heading
- AWS re:Invent, Google Cloud Next, and Microsoft Build announcements. These annual developer conferences are where the cloud providers announce new infrastructure services — each new service represents years of engineering work and creates ongoing engineering employment
- FinOps Foundation and cloud cost optimization trends. As cloud spending grows, so does demand for engineers who optimize it. Cloud cost engineering is an emerging specialty where infrastructure knowledge and financial modeling intersect
The career path has no ceiling that most people are likely to reach. The technical problems of operating computing infrastructure at global scale are genuinely among the hardest in engineering. The compensation reflects that. And the companies that employ these engineers are building the foundation that everything else in technology runs on.
FAQ
Is cloud engineering the same as software engineering? Related but distinct. Software engineers build applications. Cloud infrastructure engineers build the systems that applications run on. The skills overlap significantly — infrastructure engineers write extensive code — but the focus on systems reliability, distributed computing, and operational concerns differentiates the field.
Do you need to work at AWS, Azure, or Google to do this work? No. Cloud infrastructure engineering skills apply at thousands of companies that build internal platforms for their own software. Companies like Netflix, Spotify, Stripe, Airbnb, and thousands of others have internal infrastructure engineering teams that work on problems of comparable difficulty to the cloud providers themselves.
How does AI affect cloud infrastructure careers? AI dramatically increases infrastructure demand, creating more work for cloud engineers. It also introduces new types of infrastructure problems — large-scale model training, AI inference serving, vector database management — that require specialized expertise. Net effect: more demand, not less.
Is there a certification path for this career? Yes. AWS, Azure, and Google all have certification programs ranging from associate to professional and specialty levels. AWS certifications (Solutions Architect, Developer, SysOps, DevOps Engineer) are widely recognized and respected. These are legitimate skills validators, though hands-on experience with real systems is more valuable than certifications alone.
What’s the work-life balance like? Cloud infrastructure teams carry on-call responsibilities because systems must be available 24/7. This is a genuine tradeoff — the compensation reflects the responsibility. Most large cloud teams rotate on-call duties and invest heavily in automation specifically to reduce the toil associated with operational work.
What’s the path for a high school student? AWS Educate and Google Cloud’s free tier both provide access to real cloud services at no cost. Building and hosting a real web application, learning the basics of virtual machines and networking, and working through AWS’s or Google’s free tutorials builds genuine skills. The AWS Certified Cloud Practitioner exam is accessible to high school students and provides a recognized credential.
About the author Ricky Flores is the founder of HiWave Makers and an electrical engineer with 15+ years of experience building consumer technology at Apple, Samsung, and Texas Instruments. He writes about how kids learn to build, think, and create in a tech-saturated world. Read more at hiwavemakers.com.
Sources
- Gartner. Magic Quadrant for Cloud Infrastructure and Platform Services. https://www.gartner.com/en/information-technology/insights/cloud-strategy
- Amazon Web Services. AWS Infrastructure Overview. https://aws.amazon.com/about-aws/global-infrastructure/
- Beyer, B. et al. Site Reliability Engineering. O’Reilly Media, 2016. https://sre.google/sre-book/table-of-contents/
- Linux Foundation. 2024 Open Source Jobs Report. https://www.linuxfoundation.org/research/the-10th-annual-open-source-jobs-report
- McKinsey Technology Council. Technology Trends Outlook 2024. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-top-trends-in-tech
- levels.fyi. Software Engineer Compensation Data. https://www.levels.fyi/
- CNCF. Cloud Native Computing Foundation Annual Survey 2023. https://www.cncf.io/reports/cncf-annual-survey-2023/
