Senior Platform / Solution Architect at Tezza Business Solutions Ltd

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. 

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.