Tezza Business Solutions Ltd is a "niche” service provider of Software Development, Quality Assurance and Software Testing services. Tezza began its operations as Web Development company in 2000 in Overland Park, Kansas. Since then, we’ve evolved into a Services-oriented company who only engage in Product development as a value-add service to our custome...

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.

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

• 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.