Cloud & Infrastructure Topics
Cloud platform services, infrastructure architecture, Infrastructure as Code, environment provisioning, and infrastructure operations. Covers cloud service selection, infrastructure provisioning patterns, container orchestration (Kubernetes), multi-cloud and hybrid architectures, infrastructure cost optimization, and cloud platform operations. For CI/CD pipeline and deployment automation, see DevOps & Release Engineering. For cloud security implementation, see Security Engineering & Operations. For data infrastructure design, see Data Engineering & Analytics Infrastructure.
Infrastructure Strategy and Platform Decisions
Focuses on making technical infrastructure and platform choices with consideration for business impact and organizational factors. Topics include build versus buy trade offs, vendor and platform evaluation, scalability and reliability considerations, migration and deprecation planning for legacy systems, total cost of ownership, developer productivity impact, organizational readiness, and stakeholder involvement. Candidates should show how to structure these decisions, evaluate technical and non technical risks, and communicate clear rationale and implementation plans.
Cloud Platform Experience
Personal account of hands on experience using public cloud providers and the concrete results delivered. Candidates should describe specific services and patterns they used for compute, storage, networking, managed databases, serverless and eventing, and explain their role in architecture decisions, deployments, automation and infrastructure as code practices, continuous integration and continuous delivery pipelines, container orchestration, scaling and performance tuning, monitoring and incident response, and cost management. Interviewees should quantify outcomes when possible with metrics such as latency reduction, cost savings, availability improvements or deployment frequency and note any formal training or certifications. This topic evaluates depth of practical experience, ownership, and the ability to operate and improve cloud systems in production.
Technical Vision and Infrastructure Roadmap
This topic assesses a candidate's ability to define a multi year technical vision for infrastructure, platform, and systems and to translate that vision into a practical execution roadmap. Core skills include evaluating technology choices and architecture evolution, planning migration and modernization paths, anticipating scalability and capacity needs, and balancing cost performance with resilience and operational reliability. Candidates should demonstrate approaches to managing technical debt, sequencing investments across quarters and releases, estimating resources and timelines, establishing measurable infrastructure goals and key performance indicators, and implementing governance and standards. Discussion may also cover reliability and observability, security and compliance considerations, trade offs between short term stability and long term rearchitecture, prioritization to enable business outcomes, and communicating technical trade offs to both technical and non technical stakeholders.
Build vs. Buy vs. Cloud vs. On Premise Trade Offs
Understanding key trade-offs in technology decision-making: (1) Build vs. Buy - custom development flexibility vs. packaged software speed/cost, (2) Cloud vs. On-Premise - operational burden, control, scalability, security, cost, (3) SaaS vs. Licensed - flexibility, upgrade frequency, customization options. Understanding implications for cost, time-to-value, flexibility, control, and ongoing support.
Capacity Planning and Resource Optimization
Covers forecasting, provisioning, and operating compute, memory, storage, and network resources efficiently to meet demand and service level objectives. Key skills include monitoring resource utilization metrics such as central processing unit usage, memory consumption, storage input and output and network throughput; analyzing historical trends and workload patterns to predict future demand; and planning capacity additions, safety margins, and buffer sizing. Candidates should understand vertical versus horizontal scaling, autoscaling policy design and cooldowns, right sizing instances or containers, workload placement and isolation, load balancing algorithms, and use of spot or preemptible capacity for interruptible workloads. Practical topics include storage planning and archival strategies, database memory tuning and buffer sizing, batching and off peak processing, model compression and inference optimization for machine learning workloads, alerts and dashboards, stress and validation testing of planned changes, and methods to measure that capacity decisions meet both performance and cost objectives.
Cost Optimization at Scale
Addresses cost conscious design and operational practices for systems operating at large scale and high volume. Candidates should discuss measuring and improving unit economics such as cost per request or cost per customer, multi tier storage strategies and lifecycle management, caching, batching and request consolidation to reduce resource use, data and model compression, optimizing network and input output patterns, and minimizing egress and transfer charges. Senior discussions include product level trade offs, prioritization of cost reductions versus feature velocity, instrumentation and observability for ongoing cost measurement, automation and runbook approaches to enforce cost controls, and organizational practices to continuously identify, quantify, and implement savings without compromising critical service level objectives. The topic emphasizes measurement, benchmarking, risk assessment, and communicating expected savings and operational impacts to stakeholders.
Infrastructure Implementation and Operations
Hands on design, deployment, and operational management of infrastructure components and services. This includes setting up and configuring load balancers, database replication and high availability, caching layers, networking and network security, service discovery and routing, container deployment and orchestration, monitoring and observability, logging and alerting, backup and disaster recovery strategies, and secrets management in runtime. Candidates should be able to walk through concrete implementations, explain trade offs, demonstrate troubleshooting and performance tuning, and show how infrastructure components integrate to meet availability, scalability, and security requirements.
Platform Architecture for Organizational Scale
Designing internal platforms and infrastructure to support large engineering organizations and evolving teams. Topics include developer experience and self service platform design, deployment platforms that enable safe frequent releases for hundreds of engineers, platform automation and observability patterns that provide cross service visibility, governance and operational policies, service onboarding and lifecycle, and how to evolve platform capabilities as headcount and service count grows. Candidates should discuss trade offs between centralized platform services and team autonomy, metrics for platform health, and approaches to encourage adoption while minimizing operational friction.
Google Cloud Platform Deep Dive
In depth coverage of Google Cloud Platform services across compute, networking, storage, orchestration, and platform integrations. Areas include Compute Engine instance management and machine type selection, Google Kubernetes Engine concepts for container orchestration, managed databases such as Cloud SQL and Firestore, Cloud Storage features including versioning and lifecycle, networking components including Virtual Private Cloud, VPN and load balancing, content delivery with Cloud CDN, eventing and messaging with Pub/Sub, and analytics with BigQuery. Candidates should demonstrate design decisions, operational practices, scaling strategies, security and identity considerations, and service limits and trade offs for production deployments.