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Job Description

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

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 / 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.

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, N+1 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, Flood.io, GitHub Copilot, Testim AI, Mabl, Functionize, Python, Java, Node.js, 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

About Company

Smart Ship© Hub is a fast-growing digital platform for global maritime. Headquartered in Singapore and with its engineering cum customer support Centre in India, Smart Ship Hub is top 5 digital transformation company in the world. Its clients include 2 of the world’s top 5 maritime companies and others who are leading in specific regions. We are part of the largest transport mode; maritime transport is a 20 Trillion $ global industry and undertakes about 90% of global trade volume.

Smart Ship Hub partners with the most reputed names in the industry including across Singapore, Japan, Europe, Middle East, India. Our digital platform aimed at merchant ships, oil rigs, offshore facilities, tug and barges, fishing vessels, ferries, RORO’s includes every ocean and river-based assets. The proprietary platform leverages the power of IOT, Big data, ML and Mobility to bring in unique experience of predictive diagnostics, condition and health monitoring, remote monitoring – management and control.

We help our customers to save millions of dollars and enhance their operational efficiencies by up to 30%.

Smart Ship© Hub has its offices in Singapore and Pune, India and global network of engineering support services across 4 locations (China, Dubai, Rotterdam, Japan). We are in process of setting up of our Japan operations in Tokyo.

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Sr QA Automation Engineer

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