Smart Ship© Hub
Website:
smartshiphub.com
Job details:
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, Node.js, 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
- 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
- Protocol coverage: HTTP/HTTPS REST, gRPC, WebSocket, MQTT (IoT telemetry), GraphQL, Server-Sent Events (SSE) — performance testing beyond simple HTTP
- Realistic data generation: parameterised virtual user datasets, correlated parameters (login → session token → API call chains), data pool management
- Correlation and dynamic extraction: response token extraction, session correlation, JSON/XML/regex extractors — no hard-coded test values
- Think time and pacing: realistic inter-request delays, user pacing, constant throughput timers — preventing artificial test results
- Performance test environment management: traffic shaping, network throttling simulation, database seeding with production-scale data volumes
- Database Performance & Query Optimisation Testing
- PostgreSQL performance testing: EXPLAIN / EXPLAIN ANALYSE, query plan inspection, index usage validation, connection pool benchmarking (PgBouncer)
- MongoDB performance: aggregation pipeline performance analysis, index strategy validation, read/write throughput benchmarking under shard-scale data volumes
- Time-series databases: TimescaleDB / InfluxDB / BigQuery — benchmarking ingestion throughput for IoT telemetry (vessel sensor data at fleet scale)
- Database load simulation: realistic concurrent query load generation (pgbench, mongo-perf, custom Python/Java load drivers) replicating production query mix
- Slow query identification: automated slow query log analysis, long-running transaction detection, lock contention identification
- N+1 query detection: integration of database call tracing into API performance tests — identify ORM-generated N+1 patterns under load
- Connection pool exhaustion testing: simulate pool saturation, queue depth, connection timeout behaviour under peak concurrency
- Database backup performance: validate backup and restore duration under production data volumes against RTO/RPO targets
- Data volume scaling: populate test environments with statistically representative data volumes (millions of vessel events, port calls, voyage records)
- Cloud Scale Testing & Infrastructure Validation
- 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
- Storage performance: GCS / S3 / Azure Blob throughput testing; PVC / persistent volume I/O benchmarking in Kubernetes
- 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
- Gen AI — Augmented Test Design & Automation (Mandatory)
- Claude (Anthropic): using Claude API / claude.ai 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, Mabl, Functionize, Applitools) and evaluating their applicability to performance use cases
- Performance Observability, Metrics & Analysis
- Prometheus: custom metrics instrumentation (client libraries for Java / Python / Node.js), performance-relevant alerting rules, histogram / percentile analysis
- Grafana: performance test result dashboards (live during test execution), SLO compliance dashboards, regression comparison across test runs
- Distributed tracing: OpenTelemetry / Jaeger / Zipkin — trace slow paths under load, identify latency contributions per microservice during test execution
- APM integration: Datadog / New Relic / Dynatrace — correlate load test activity with APM traces for root cause identification
- JMeter / k6 result analysis: response time percentile analysis (p50, p90, p95, p99), error rate analysis, throughput vs latency correlation
- Flame graph analysis: async-profiler / perf / py-spy — CPU and memory hotspot identification under load in JVM and Python services
- Statistical analysis: performance regression detection using Mann-Whitney U test or t-test — automated baseline comparison in CI pipeline
- Performance test reporting: executive summary generation (automated via Gen AI), trend reports, capacity planning recommendations
- CI/CD Pipeline Integration & Shift-Left Performance
- 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
- Cloud-Native Product Testing Expertise
- REST API performance testing: complete CRUD lifecycle load testing, pagination performance, bulk operation throughput, concurrent user simulation
- WebSocket / real-time performance: sustained connection load testing (vessel tracking dashboards receiving live AIS updates), message delivery latency validation
- Multi-tenancy performance isolation: validate that one tenant's load does not degrade other tenants' performance — noisy-neighbour detection
- SLA / SLO validation: automated validation that p99 API response times meet contractual SLAs under defined load profiles
- Geographical latency testing: Cloud-native load generation from GCP / AWS regions closest to vessel fleet locations; latency profiling per maritime route
- Serverless cold-start benchmarking: Cloud Functions / Lambda cold-start latency measurement and optimisation impact validation
Key Responsibilities
Performance Engineering Ownership
- 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)
- Validate BCP/DR database failover performance — measure RTO/RPO achievement under controlled failure scenarios
Gen AI Integration
- 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 / Flood.io (cloud load generation)
Gen AI & Automation
- Claude (Anthropic API / claude.ai)
- ChatGPT / GPT-4o (OpenAI API)
- GitHub Copilot (AI code completion)
- Testim AI, Mabl, Functionize (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.
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