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.
Hardware Troubleshooting and Diagnostics
Skills and processes for detecting, diagnosing, and remediating hardware faults across servers, storage, network interface cards, and peripherals. Topics include recognizing common failure modes for CPU, memory, disk, and NICs; using firmware and BIOS UEFI settings and diagnostics; interpreting hardware monitoring outputs and sensor data; running vendor diagnostic utilities and benchmarks; using SMART and storage diagnostics; distinguishing hardware from software issues; safe escalation and vendor engagement procedures; and planning replacements and mitigations to avoid data loss or downtime. Emphasis is on methodical data collection, reproducible tests, and appropriate escalation.
Containerization and Virtualization Trade Offs
Examines trade offs between containers and virtual machines and the complexity of orchestrated environments. Topics include hypervisor and virtual machine basics, container isolation and resource models, performance and overhead comparisons, security and attack surface differences, when to prefer virtual machines versus containers, single container versus orchestrated multi container setups, operational complexity versus benefits, and criteria for selecting the appropriate platform at different scales.
Systems Scope and Platform Experience
Communication of the technical domains and operational environments a candidate has worked with, including hardware platforms, operating systems, networking environments, middleware and enterprise applications. Candidates should quantify scale where possible, for example number of users supported, number of systems administered, volume of data processed, or ticket throughput. The topic covers exposure to deployment environments, monitoring and maintenance responsibilities, and the boundaries of systems the candidate has owned or contributed to.
System Administration Tools and Monitoring
Knowledge of monitoring, logging, and operational tooling used to observe and manage infrastructure. Includes monitoring platforms such as Nagios, Zabbix, and Prometheus; log aggregation and search with ELK Stack or similar; metrics collection, time series databases, alerting rules, dashboards and visualization, instrumenting systems and services for observability, common system utilities such as top, iostat, sar, and vmstat, integration with incident management and ticketing systems, and determining which metrics matter for capacity and performance monitoring.
Observability and Monitoring Architecture
Designing and architecting end to end observability and monitoring systems that scale, remain reliable under load, and do not become single points of failure. Topics include deciding which telemetry to collect and why including metrics logs traces and events, instrumentation strategies, collection models such as push versus pull, high throughput telemetry ingestion and pipeline design, time series storage and compression, aggregation and partitioning strategies, metric cardinality and retention tradeoffs, distributed tracing propagation and sampling strategies, log aggregation and secure storage, selection of storage backends and time series databases, storage tiering and cost optimization, query and dashboard performance considerations, access control and multi tenancy, integration with deployment pipelines and tooling, and design patterns for self healing telemetry pipelines. Senior level assessments include designing scalable ingestion and aggregation architectures, storage tiering and query performance optimization, cost and operational tradeoffs, and organizational impacts of observability data.
Linux System Administration
Linux specific system administration and deep operating system topics. Areas include Linux kernel concepts, process lifecycle and signals, memory management and swap behavior in Linux, Linux file systems and permission models, boot processes and init systems such as systemd, package management and software installation, service management and system daemons, shell and scripting for automation and debugging, performance tuning and profiling, log management and diagnostic techniques, security and access control on Linux, and approaches to investigating and resolving systemic failures in Linux environments. At senior levels candidates should demonstrate both operational competence and an understanding of internal mechanisms and trade offs.
Virtualization and Cloud Services Basics
Basic understanding of virtual machines, VMs vs. physical systems, cloud infrastructure (IaaS, PaaS), and how cloud services affect support work. Understanding when issues might be cloud-related vs. local system issues.
Backup and Recovery Fundamentals
Core principles and techniques for protecting, storing, and restoring data. Topics include backup models and types such as full, incremental, and differential backups; snapshotting and replication approaches; transaction log management and write ahead logging for databases; point in time recovery and continuous log based recovery; trade offs between Recovery Time Objective and Recovery Point Objective and how business requirements drive those trade offs; backup storage architectures for file systems and databases including local, off site, and geo redundant storage; backup scheduling, retention lifecycle and policy governance including encryption and compliance; and verification and integrity checks to ensure backups are restorable. Candidates should be able to describe step by step restore procedures, validation and testing techniques, common failure scenarios, and decision trade offs when choosing backup approaches for different application types.
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.