The Senior QA Automation Engineer (Performance Database & Scale Testing) is responsible for designing building and operating the performance and scale testing capability across SmartShipHub's cloud-native platform. The role owns the full performance engineering lifecycle — from requirements and test design through execution analysis and actionable recommendations to engineering teams.
The successful candidate will architect reusable maintainable performance test frameworks; build AI-augmented test generation pipelines leveraging Gen AI tooling; validate database query performance under realistic fleet-scale loads; and champion performance as a first-class quality gate in CI/CD pipelines.
JMeter / Gatling / k6
Locust / Artillery
Prometheus / Grafana
PostgreSQL / MongoDB
Cloud: GCP / AWS / Azure
Gen AI: Claude / ChatGPT
K8s Scale Testing
Database Benchmarking
CI/CD Integration
IoT / Maritime SaaS
Experience Requirements
Core Experience
7–12 years of total QA / quality engineering experience with meaningful progression
5 years of hands-on performance load and scale testing experience in cloud-native SaaS or product-based environments
Proven experience building performance test frameworks from scratch — not just running existing scripts
Experience at a product-based company or MNC with global SLAs and high-availability commitments is strongly preferred
Experience contributing to or owning performance engineering as a dedicated function (not combined QA generalist)
Domain & Environment
Experience testing cloud-based products deployed on GCP AWS or Azure — Kubernetes-hosted microservices strongly preferred
Experience with IoT maritime industrial or high-volume telemetry platforms is a significant plus
Comfort working in polyglot engineering environments (Java Python Go) across test tooling
Experience collaborating with SRE platform and database engineering teams on performance RCA
Exposure to maritime vessel data AIS feeds or time-series telemetry data pipelines is a bonus.
Technical Skills & Competencies
1. Performance & Load Testing Frameworks
Expert-level proficiency with two or more: Apache JMeter Gatling k6 Locust Artillery Tsung — test plan design parameterisation distributed execution
Test scenario design: steady-state load ramp-up/ramp-down spike tests soak tests breakpoint / stress tests — mapped to real user journey profiles
Distributed load generation: JMeter master-slave clusters on Kubernetes; k6 operator on GKE/EKS; Artillery cloud generating realistic global load from multiple regions
Kubernetes scale testing: HPA (Horizontal Pod Autoscaler) validation — trigger scale-up latency scale-down behaviour; PodDisruptionBudget compliance under load
Cloud service limits: API rate limit validation cloud quota headroom testing throttling behaviour under burst traffic
Multi-region load testing: latency profiling across GCP / AWS / Azure regions; CDN / global load balancer behaviour validation
Chaos engineering for performance: Chaos Mesh / LitmusChaos — pod kill network latency injection disk I/O stress during load tests to validate graceful degradation
Message queue throughput: Kafka / Pub/Sub / RabbitMQ consumer lag benchmarking; producer throughput at fleet-scale IoT message rates
Cache performance validation: Redis / Memcached hit ratio eviction rate latency percentiles under production-representative cache workloads
CDN and edge performance: cache-hit rate validation origin offload percentage global TTFB (Time to First Byte) benchmarking
Autoscaling cost analysis: correlate scale events with cloud billing impact — performance vs cost trade-off reporting
4. Gen AI — Augmented Test Design & Automation (Mandatory)
Claude (Anthropic): using Claude API / to generate realistic load test scripts from API specifications (OpenAPI / Swagger) user story descriptions or production traffic samples
ChatGPT / GPT-4o: prompt engineering for test scenario generation edge case identification performance test data synthesis test result narrative generation
AI-assisted test code generation: using LLM prompts to generate k6 / JMeter / Gatling scripts database test queries and data seed scripts — with human review and validation
AI for anomaly analysis: using LLMs to analyse Grafana/Prometheus alert output slow query logs and JMeter result summaries — generate natural-language RCA narratives
Prompt engineering discipline: structured prompts for consistent reproducible test artefact generation; version-controlled prompt libraries; output validation pipelines
AI-powered test maintenance: LLM-based script update generation when APIs change — diff-aware prompt templates that preserve existing scenario logic
Gen AI evaluation: critical evaluation of LLM-generated test output for correctness security (no credential leakage) and representativeness — AI assists engineer decides
Emerging tooling: staying current with AI-native testing tools (Testim AI MablFunctionizeApplitools) and evaluating their applicability to performance use cases
GitHub Actions / GitLab CI: integrate k6 / Gatling performance tests as CI pipeline stages — block merges on performance regression (p99 threshold breach)
Performance baselines: automated baseline capture on main branch; PR-triggered comparative tests with pass/fail decision against baseline
Performance budget enforcement: define and enforce response time throughput and error rate budgets per API endpoint — surfaced as PR status checks
Staging environment performance gates: automated nightly performance regression suite in staging — failures block production deployments
Artefact management: performance test scripts versioned in git; test result artefacts stored in GCS / S3 with retention policies; baseline database in Postgres
Container-native test execution: k6 operator / JMeter Docker containers orchestrated in Kubernetes — ephemeral reproducible test infrastructure
7. Cloud-Native Product Testing Expertise
REST API performance testing: complete CRUD lifecycle load testing pagination performance bulk operation throughput concurrent user simulation
Own the end-to-end performance test strategy for SmartShipHub's cloud platform — from SLA definition through test design execution analysis and remediation tracking
Build and maintain reusable modular performance test frameworks covering API database messaging and WebSocket layers
Establish and maintain performance baselines per sprint; detect and report regressions before they reach production
Define performance NFRs (Non-Functional Requirements) in collaboration with product and engineering — translate into testable acceptance criteria
Lead performance RCA investigations — coordinate with platform database and backend engineering to resolve identified bottlenecks
Database & Scale Testing
Design and execute database performance test suites for PostgreSQL MongoDB and time-series stores — query benchmarking index validation connection pool testing
Populate test environments with statistically representative production-scale data volumes — automated data seeding pipelines
Validate scale-out behaviour of Kubernetes-hosted services under fleet-scale IoT telemetry loads (millions of vessel events per hour)
Build and maintain Gen AI–augmented test generation pipelines (Claude ChatGPT) — accelerating test script creation data synthesis and RCA reporting
Develop prompt engineering standards and libraries for performance test artefact generation — version-controlled reviewed and auditable
Evaluate and adopt emerging AI-native testing tools appropriate for SmartShipHub's platform context
Advocate for responsible AI use in testing — ensuring LLM-generated output is validated not blindly trusted
Tools & Technology Stac
Performance & Testing Tools
• Apache JMeter Gatling k6 Locust Artillery
• pgbench mongo-perf TimescaleDB bench
• Chaos Mesh LitmusChaos
• OpenTelemetry Jaeger Zipkin
• Datadog / New Relic / Dynatrace APM
• async-profiler py-spy (flame graphs)
• Prometheus Grafana (dashboards)
• BlazeMeter / (cloud load generation)
Gen AI & Automation
• Claude (Anthropic API / )
• ChatGPT / GPT-4o (OpenAI API)
• GitHub Copilot (AI code completion)
• Testim AI MablFunctionize (evaluation)
• Python / JavaScript / Java (test frameworks)
• GitHub Actions GitLab CI (pipeline integration)
• Docker / Kubernetes (test infrastructure)
• GCP / AWS / Azure (cloud environments)
SmartShipHub is an equal-opportunity employer. All applicants are considered regardless of race gender religion nationality disability or age.
Required Skills:
Apache JMeter Gatling k6 Locust Artillery Tsung Distributed Load Generation HTTP/HTTPS REST gRPC WebSocket MQTT GraphQL Server-Sent Events Realistic Data Generation Parameterisation Correlation Dynamic Extraction Think Time Pacing Traffic Shaping Network Throttling Database Seeding PostgreSQL EXPLAIN EXPLAIN ANALYZE Query Plan Inspection Index Usage Validation Connection Pool Benchmarking PgBouncer MongoDB Aggregation Pipeline Performance Analysis Index Strategy Validation Read/Write Throughput Benchmarking TimescaleDB InfluxDB BigQuery Database Load Simulation pgbench mongo-perf Slow Query Identification Slow Query Log Analysis Long-Running Transaction Detection Lock Contention Identification N1 Query Detection Connection Pool Exhaustion Testing Database Backup Performance Data Volume Scaling Kubernetes Scale Testing HPA Validation PodDisruptionBudget Cloud Service Limits Validation Multi-Region Load Testing Latency Profiling Chaos Engineering Chaos Mesh LitmusChaos Storage Performance Testing GCS S3 Azure Blob PVC I/O Benchmarking Message Queue Throughput Kafka Pub/Sub RabbitMQ Cache Performance Validation Redis Memcached CDN Performance Cache-Hit Rate Origin Offload TTFB Autoscaling Cost Analysis Gen AI Claude ChatGPT AI-Assisted Test Code Generation Prompt Engineering AI for Anomaly Analysis Gen AI Evaluation Performance Observability Prometheus Custom Metrics Instrumentation Grafana Dashboards Distributed Tracing OpenTelemetry Jaeger Zipkin APM Integration Datadog New Relic Dynatrace JMeter Result Analysis k6 Result Analysis Response Time Percentile Analysis Error Rate Analysis Throughput vs Latency Correlation Flame Graph Analysis async-profiler py-spy Statistical Analysis Mann-Whitney U Test t-test Performance Regression Detection CI/CD Integration GitHub Actions GitLab CI Performance Baselines Performance Budget Enforcement Staging Environment Performance Gates Artefact Management Container-Native Test Execution Docker Kubernetes Cloud-Native Product Testing REST API Performance Testing WebSocket Performance Testing Multi-Tenancy Performance Isolation SLA/SLO Validation Geographical Latency Testing Serverless Cold-Start Benchmarking BlazeMeter GitHub Copilot Testim AI Mabl Functionize Python Java Go Test Framework Design Performance RCA Cross-Functional Collaboration Communication Capacity Planning IoT Maritime Industrial Telemetry AIS Feeds Time-Series Telemetry Performance Engineering Load Testing Scale Testing Database Testing Cloud Testing API Testing Microservices Testing Performance Budgeting Shift-Left Performance Performance NFRs Non-Functional Requirements
Role OverviewThe Senior QA Automation Engineer (Performance Database & Scale Testing) is responsible for designing building and operating the performance and scale testing capability across SmartShipHub's cloud-native platform. The role owns the full performance engineering lifecycle — from requirem...
Role Overview
The Senior QA Automation Engineer (Performance Database & Scale Testing) is responsible for designing building and operating the performance and scale testing capability across SmartShipHub's cloud-native platform. The role owns the full performance engineering lifecycle — from requirements and test design through execution analysis and actionable recommendations to engineering teams.
The successful candidate will architect reusable maintainable performance test frameworks; build AI-augmented test generation pipelines leveraging Gen AI tooling; validate database query performance under realistic fleet-scale loads; and champion performance as a first-class quality gate in CI/CD pipelines.
JMeter / Gatling / k6
Locust / Artillery
Prometheus / Grafana
PostgreSQL / MongoDB
Cloud: GCP / AWS / Azure
Gen AI: Claude / ChatGPT
K8s Scale Testing
Database Benchmarking
CI/CD Integration
IoT / Maritime SaaS
Experience Requirements
Core Experience
7–12 years of total QA / quality engineering experience with meaningful progression
5 years of hands-on performance load and scale testing experience in cloud-native SaaS or product-based environments
Proven experience building performance test frameworks from scratch — not just running existing scripts
Experience at a product-based company or MNC with global SLAs and high-availability commitments is strongly preferred
Experience contributing to or owning performance engineering as a dedicated function (not combined QA generalist)
Domain & Environment
Experience testing cloud-based products deployed on GCP AWS or Azure — Kubernetes-hosted microservices strongly preferred
Experience with IoT maritime industrial or high-volume telemetry platforms is a significant plus
Comfort working in polyglot engineering environments (Java Python Go) across test tooling
Experience collaborating with SRE platform and database engineering teams on performance RCA
Exposure to maritime vessel data AIS feeds or time-series telemetry data pipelines is a bonus.
Technical Skills & Competencies
1. Performance & Load Testing Frameworks
Expert-level proficiency with two or more: Apache JMeter Gatling k6 Locust Artillery Tsung — test plan design parameterisation distributed execution
Test scenario design: steady-state load ramp-up/ramp-down spike tests soak tests breakpoint / stress tests — mapped to real user journey profiles
Distributed load generation: JMeter master-slave clusters on Kubernetes; k6 operator on GKE/EKS; Artillery cloud generating realistic global load from multiple regions
Kubernetes scale testing: HPA (Horizontal Pod Autoscaler) validation — trigger scale-up latency scale-down behaviour; PodDisruptionBudget compliance under load
Cloud service limits: API rate limit validation cloud quota headroom testing throttling behaviour under burst traffic
Multi-region load testing: latency profiling across GCP / AWS / Azure regions; CDN / global load balancer behaviour validation
Chaos engineering for performance: Chaos Mesh / LitmusChaos — pod kill network latency injection disk I/O stress during load tests to validate graceful degradation
Message queue throughput: Kafka / Pub/Sub / RabbitMQ consumer lag benchmarking; producer throughput at fleet-scale IoT message rates
Cache performance validation: Redis / Memcached hit ratio eviction rate latency percentiles under production-representative cache workloads
CDN and edge performance: cache-hit rate validation origin offload percentage global TTFB (Time to First Byte) benchmarking
Autoscaling cost analysis: correlate scale events with cloud billing impact — performance vs cost trade-off reporting
4. Gen AI — Augmented Test Design & Automation (Mandatory)
Claude (Anthropic): using Claude API / to generate realistic load test scripts from API specifications (OpenAPI / Swagger) user story descriptions or production traffic samples
ChatGPT / GPT-4o: prompt engineering for test scenario generation edge case identification performance test data synthesis test result narrative generation
AI-assisted test code generation: using LLM prompts to generate k6 / JMeter / Gatling scripts database test queries and data seed scripts — with human review and validation
AI for anomaly analysis: using LLMs to analyse Grafana/Prometheus alert output slow query logs and JMeter result summaries — generate natural-language RCA narratives
Prompt engineering discipline: structured prompts for consistent reproducible test artefact generation; version-controlled prompt libraries; output validation pipelines
AI-powered test maintenance: LLM-based script update generation when APIs change — diff-aware prompt templates that preserve existing scenario logic
Gen AI evaluation: critical evaluation of LLM-generated test output for correctness security (no credential leakage) and representativeness — AI assists engineer decides
Emerging tooling: staying current with AI-native testing tools (Testim AI MablFunctionizeApplitools) and evaluating their applicability to performance use cases
GitHub Actions / GitLab CI: integrate k6 / Gatling performance tests as CI pipeline stages — block merges on performance regression (p99 threshold breach)
Performance baselines: automated baseline capture on main branch; PR-triggered comparative tests with pass/fail decision against baseline
Performance budget enforcement: define and enforce response time throughput and error rate budgets per API endpoint — surfaced as PR status checks
Staging environment performance gates: automated nightly performance regression suite in staging — failures block production deployments
Artefact management: performance test scripts versioned in git; test result artefacts stored in GCS / S3 with retention policies; baseline database in Postgres
Container-native test execution: k6 operator / JMeter Docker containers orchestrated in Kubernetes — ephemeral reproducible test infrastructure
7. Cloud-Native Product Testing Expertise
REST API performance testing: complete CRUD lifecycle load testing pagination performance bulk operation throughput concurrent user simulation
Own the end-to-end performance test strategy for SmartShipHub's cloud platform — from SLA definition through test design execution analysis and remediation tracking
Build and maintain reusable modular performance test frameworks covering API database messaging and WebSocket layers
Establish and maintain performance baselines per sprint; detect and report regressions before they reach production
Define performance NFRs (Non-Functional Requirements) in collaboration with product and engineering — translate into testable acceptance criteria
Lead performance RCA investigations — coordinate with platform database and backend engineering to resolve identified bottlenecks
Database & Scale Testing
Design and execute database performance test suites for PostgreSQL MongoDB and time-series stores — query benchmarking index validation connection pool testing
Populate test environments with statistically representative production-scale data volumes — automated data seeding pipelines
Validate scale-out behaviour of Kubernetes-hosted services under fleet-scale IoT telemetry loads (millions of vessel events per hour)
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