Senior Platform/ Solution Architect

Experience & Professional Profile

• Typically 10–15+ years of experience across solution architecture, platform architecture, cloud infrastructure, capacity planning, performance engineering or related disciplines.
  
• Proven experience designing and dimensioning large-scale distributed systems and microservices platforms.
  
• Strong experience with Kubernetes/OpenShift and containerised application environments.
  
• Hands-on experience with cloud and on-premises infrastructure architecture.
  
• Demonstrable experience in capacity planning, workload modelling, performance engineering and infrastructure forecasting.
  
• Experience with large-scale, high-availability, transaction-intensive environments is highly desirable.
  
• Experience in telecoms, financial services, digital platforms or other high-volume technology environments is advantageous.
  

Core Responsibilities

• Lead capacity planning and infrastructure dimensioning for applications, platforms and microservices-based services.
  
• Translate business growth, transaction volumes and traffic forecasts into infrastructure capacity requirements.
  
• Develop quantitative workload models covering normal, peak, burst and exceptional traffic conditions.
  
• Determine appropriate CPU, memory, pod/replica, node and cluster requirements for application services.
  
• Develop capacity forecasts and infrastructure roadmaps covering short-, medium- and long-term demand.
  
• Ensure capacity plans support availability, resilience, disaster recovery and business continuity requirements.
  
• Provide architecture and capacity recommendations for both cloud and on-premises environments.
  
• Review existing environments to identify over-provisioning, under-provisioning, bottlenecks and capacity risks.
  

Microservices Capacity Planning & Dimensioning

• Assess resource consumption and performance characteristics of individual microservices.
  
• Determine minimum, normal and maximum pod/replica requirements based on workload and service-level objectives.
  
• Define CPU and memory requests and limits for containers.
  
• Assess horizontal and vertical scaling requirements and define appropriate scaling policies.
  
• Determine node density, resource utilisation and cluster capacity requirements.
  
• Account for service-to-service communication, platform overhead and infrastructure reserve capacity.
  
• Establish repeatable sizing methodologies for new applications and services.
  
• Validate sizing assumptions through performance and capacity testing.
  

Capacity Planning Parameters & Metrics

• Define and maintain standard parameters for application and infrastructure capacity planning.
  
• Analyse requests per second (RPS), transactions per second (TPS), concurrent users, sessions and transaction volumes.
  
• Analyse average, peak and burst traffic and associated growth patterns.
  
• Assess CPU utilisation, CPU consumption per transaction, memory utilisation, memory peaks and application heap requirements.
  
• Assess pod counts, replica requirements, scaling thresholds and scaling response times.
  
• Determine node CPU, node memory and allocatable cluster capacity.
  
• Assess database TPS, connections, CPU, memory, IOPS and throughput.
  
• Assess storage capacity, IOPS, throughput and growth.
  
• Assess network bandwidth, latency and packet rates.
  
• Factor in high availability, N+1/N+2 resilience, disaster recovery, growth headroom and operational reserve.
  

Performance Engineering

• Lead performance engineering and capacity validation for critical applications and platforms.
  
• Define and oversee load, stress, endurance, spike, scalability and capacity testing.
  
• Analyse throughput, response time, latency, concurrency and resource utilisation.
  
• Identify application, platform, database, storage and network bottlenecks.
  
• Establish performance baselines and capacity thresholds.
  
• Use performance test results to validate CPU, memory, pod, node and cluster sizing.
  
• Work with engineering teams to optimise resource consumption and application performance.
  

Observability & Data-Driven Capacity Planning

• Use production telemetry and historical performance data to develop evidence-based capacity models.
  
• Leverage metrics, logs, traces and APM data to understand workload behaviour.
  
• Use monitoring and observability platforms such as Prometheus, Grafana, OpenTelemetry, Dynatrace, AppDynamics or equivalent tools.
  
• Correlate traffic, application performance, pod utilisation, infrastructure consumption and database performance.
  
• Establish capacity thresholds, early-warning indicators and capacity risk dashboards.
  
• Use trend analysis and forecasting to identify future infrastructure requirements before capacity constraints occur.
  

Architecture Governance & Standards

• Establish standard capacity planning and dimensioning methodologies across the organisation.
  
• Define architecture principles, sizing standards, resource profiles and capacity governance processes.
  
• Review and approve application capacity models and infrastructure sizing proposals.
  
• Ensure new services meet defined scalability, availability, performance and capacity requirements before production deployment.
  
• Establish governance for capacity reviews following major releases, traffic changes or architectural changes.
  
• Maintain architecture documentation, capacity assumptions, sizing models and decision records.