Smart Ship Hub Digital Singapore | Japan | India 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.t / Artillery hExperience 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 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 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 2. 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) 3. 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 4. 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 5. 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 6. 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 7. 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 / Current 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.

📌 Sr QA Automation Engineer (Pune)
🏢 Smart Ship Hub
📍 Pune