· Cloud Platforms & Services
Cloud Platforms & Services

Cloud Platforms & Services

Cloud platforms have become fundamental infrastructure for modern software development, with AWS dominating across multiple engineering domains. AWS appears in >75% of Cloud Services Architecture positions, >25% of DevOps roles, and >15% of Platform Engineering positions, establishing itself as the market leader. Google Cloud Platform (GCP) serves >30% of Cloud Services Architecture positions and shows strong presence in data engineering (>10% in Data Engineering), while Azure integrates deeply with Microsoft technology stacks. Cloud-specific services demonstrate specialized adoption patterns: Lambda enables serverless architectures, Kubernetes services (EKS, AKS, GKE) power container orchestration, and observability tools like CloudWatch support operational excellence. Entry-level accessibility is strongest for core AWS skills (>75% in entry-level cloud services roles), with GCP showing moderate entry opportunities (>30%). Cloud expertise has evolved from optional to essential, appearing across backend engineering, data engineering, ML operations, and DevOps tracks, fundamentally shaping modern infrastructure and deployment practices.

Major Cloud Platforms

The three dominant cloud infrastructure providers offering comprehensive compute, storage, networking, and managed services. AWS leads with exceptional market penetration across backend, data, and ML roles. GCP shows strength in data and ML workloads, while Azure serves Microsoft-centric organizations. Strong entry-level opportunities, particularly for AWS.

AWS

Very High Demand
Rank: #1
Entry-Level: High
Market-leading cloud platform in Cloud Services Architecture (>75%), Platform Engineering (>15%), DevOps (>15%), Data Engineering (>15%), MLOps (>25%), Machine Learning Engineering (>10%), Microservices Architecture (>5%), and Security Engineering (>5%). Strong entry-level demand with >75% in cloud services roles. Used for cloud infrastructure, serverless applications, data lakes, ML model deployment, container orchestration, and virtually all cloud-native architectures.

GCP

High Demand
Rank: #2
Entry-Level: Moderate
Google Cloud Platform in Cloud Services Architecture (>30%), Data Engineering (>10%), Machine Learning Engineering (>5%), MLOps (>15%), Platform Engineering (>5%), and DevOps (>5%). Moderate entry-level presence with >30% in cloud services roles. Strong in data/ML. Used for BigQuery analytics, Kubernetes (GKE), machine learning with Vertex AI, data engineering pipelines, and organizations preferring Google's data and AI capabilities.

Azure

Moderate Demand
Rank: #3
Entry-Level: Low
Microsoft's cloud platform with presence in DevOps (>5%), Cloud Services Architecture, and Microsoft-centric organizations. Lower entry-level demand. Deep Microsoft integration. Used for .NET application hosting, enterprise hybrid cloud, Active Directory integration, Azure DevOps CI/CD, organizations standardized on Microsoft technologies, and Windows-based workloads.

Serverless & Compute Services

Cloud compute services enabling code execution without server management. Lambda leads serverless adoption in AWS environments, appearing across backend and cloud engineering roles. EC2 provides traditional virtual machine infrastructure. These services are fundamental to cloud-native architectures with moderate entry-level accessibility.

Lambda

Moderate Demand
Rank: #1
Entry-Level: Low
AWS serverless compute in Cloud Services Architecture (>5%), MLOps (>5%), and event-driven architectures. Lower entry-level presence but growing. Function-as-a-Service model. Used for event-driven applications, API backends, data processing pipelines, scheduled tasks, microservices, real-time file processing, and pay-per-execution workloads without server management.

EC2

Low Demand
Rank: #2
Entry-Level: Low
AWS virtual machines in Cloud Services Architecture (>5%) and traditional cloud infrastructure. Limited explicit demand as often implied. Used for hosting applications, custom computing environments, lift-and-shift migrations, persistent workloads, and applications requiring specific OS configurations or full control over infrastructure.

Storage & Database Services

Managed storage and database services providing scalable, durable data persistence. S3 serves as universal object storage, RDS provides managed relational databases, and DynamoDB offers NoSQL at scale. These services are fundamental to cloud architectures with varying entry-level accessibility.

S3

Low Demand
Rank: #1
Entry-Level: Low
AWS object storage with presence across cloud and data roles. Lower explicit demand as fundamental AWS service. Used for data lakes, static website hosting, backup and archival, application file storage, data pipeline staging, and virtually any unstructured data storage needs in AWS environments.

RDS

Low Demand
Rank: #2
Entry-Level: Low
AWS managed relational database service in Cloud Services Architecture (>5%), Database Design & Optimization, and cloud backend roles. Limited explicit mention. Used for managed MySQL, PostgreSQL, SQL Server, Oracle databases in cloud, automated backups and patching, read replicas, and applications needing relational databases without database administration overhead.

DynamoDB

Low Demand
Rank: #3
Entry-Level: Low
AWS NoSQL database in Cloud Services Architecture (>5%), Database Design & Optimization, and Data Engineering. Limited entry-level opportunities. Serverless key-value store. Used for serverless applications, high-scale key-value workloads, gaming leaderboards, IoT data, session management, and applications requiring single-digit millisecond latency with automatic scaling.

Container Orchestration Services

Managed Kubernetes services across major cloud providers. EKS, AKS, and GKE provide enterprise-grade container orchestration without cluster management overhead. These services are increasingly central to cloud-native architectures, though typically requiring more experience with limited entry-level accessibility.

EKS

Low Demand
Rank: #1
Entry-Level: Low
AWS Elastic Kubernetes Service with presence in Platform Engineering, Cloud Services Architecture, and container-focused roles. Lower explicit demand, often implied with Kubernetes and AWS. Used for managed Kubernetes on AWS, containerized microservices, hybrid cloud deployments, and organizations running Kubernetes workloads in AWS without managing control plane.

AKS

Low Demand
Rank: #2
Entry-Level: Low
Azure Kubernetes Service with presence in Azure-centric organizations and Platform Engineering roles. Limited explicit mention. Used for managed Kubernetes on Azure, .NET microservices deployment, Azure-integrated container workloads, and organizations standardized on Microsoft cloud infrastructure.

GKE

Low Demand
Rank: #3
Entry-Level: Low
Google Kubernetes Engine with presence in GCP environments and Platform Engineering roles. Limited explicit demand. Google-originated Kubernetes service. Used for managed Kubernetes on GCP, container orchestration with GCP integration, Cloud Run compatibility, and organizations leveraging Google's Kubernetes expertise and GCP services.

Observability & Streaming Services

Cloud-native services for monitoring, logging, and real-time data streaming. CloudWatch provides AWS observability while Kinesis enables real-time data processing. These specialized services support operational excellence and event-driven architectures with niche demand patterns.

CloudWatch

Moderate Demand
Rank: #1
Entry-Level: Low
AWS monitoring and logging service in Observability & Monitoring (>5%), Cloud Services Architecture, and AWS operational contexts. Lower entry-level demand. Built-in AWS observability. Used for AWS resource monitoring, log aggregation and analysis, custom metrics and alarms, Lambda function monitoring, dashboard creation, and operational visibility into AWS infrastructure and applications.

Kinesis

Low Demand
Rank: #2
Entry-Level: Low
AWS real-time data streaming service with limited explicit presence in Data Engineering, Real-time & Streaming Systems, and event-driven architectures (<5% prevalence). Used for real-time data ingestion, streaming ETL pipelines, clickstream analytics, log and event streaming, IoT data collection, and building real-time applications on AWS.