Senior Platform/Solution Architect
Tezza Business Solutions Ltd
Remote
Senior Platform/Solution Architect – Capacity Planning, Performance & Cloud Infrastructure Architecture
Role Purpose
The Senior Platform/Solution Architect – Capacity Planning, Performance & Cloud Infrastructure Architecture is responsible for translating business demand, application traffic and workload characteristics into quantifiable infrastructure requirements across microservices, Kubernetes/OpenShift, cloud and on-premises environments. The role provides the technical capability to determine CPU, memory, pod/replica, node, cluster, database, storage and network requirements while ensuring performance, scalability, resilience, availability and cost efficiency.
- 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.
- Key Deliverables
- Application Capacity Model
- Microservices Dimensioning Model
- CPU & Memory Sizing Model
- Pod/Replica Sizing Model
- Kubernetes/OpenShift Cluster Sizing
- Database Capacity Model
- Storage & IOPS Capacity Model
- Network Capacity Model
- Cloud Infrastructure Sizing
- On-Premises Infrastructure Sizing
- Three- to Five-Year Capacity Forecast
- Peak/Event Capacity Plan
- Performance Test Strategy and Capacity Validation Report
- Capacity and Performance Dashboard
- Infrastructure Bill of Materials (BoM)
- Cloud Cost/TCO Model
- Capacity Headroom and Risk Assessment
- 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.