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Senior PlatformSolution Architect
Job Location:
Monthly Salary:
Posted:
15 August 2026 (12 hours ago)
Posted:
Application Deadline:
12 November 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Job Description
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.
1. 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.
2. 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.
3. 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 N1/N2 resilience disaster recovery growth headroom and operational reserve.
4. 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.
5. 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.
6. 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.
7. 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
8. Experience & Professional Profile
Typically 1015 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.
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.
1. 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.
2. 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.
3. 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 N1/N2 resilience disaster recovery growth headroom and operational reserve.
4. 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.
5. 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.
6. 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.
7. 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
8. Experience & Professional Profile
Typically 1015 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.
Required Experience:
Senior IC
About Company
Offering End-to-End IT & Business Value – From Promise to Proof. Our solutions and services are designed to improve quality, drive customer engagement, ...
