Senior Platform/Solution Architect

Tezza Business Solutions LLC

🇳🇬 Nigeria

Senior

1 month ago

Microservices
Kubernetes
OpenShift
Node.js
Disaster Recovery
Prometheus
Grafana
OpenTelemetry
Dynatrace

1 month ago

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Job Description
Work Mode: Onsite
Job Type: Contract
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, N+1/N+2 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 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.

Senior Platform/Solution Architect · Tezza Business Solutions LLC

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