Key Responsibilities:
Test Automation & Framework Architecture
- Architect and maintain robust, reusable test automation frameworks for UI, API, and backend systems.
- Develop automated test suites using Playwright (primary), Selenium, PyTest, JUnit, or equivalent.
- Implement Page Object Model, Screenplay, or component-based design patterns for maintainable test code.
- Build data-driven and keyword-driven testing capabilities to support complex business scenarios.
- Integrate automated tests into CI/CD pipelines using Jenkins, GitHub Actions, or similar platforms.
- Implement shift-left testing practices — embedding quality gates early in the development lifecycle.
- Complete all assigned, mandatory training within the timeframe provided
- Conduct and/or participate in regularly scheduled 1:1 meetings with direct manager and/or direct reports
Performance, Load & Scalability Engineering
- Design and execute performance test strategies for critical systems: load testing, stress testing, spike testing, soak testing, and capacity planning.
- Build and maintain performance test suites using JMeter, Gatling, k6, Locust, or equivalent tools.
- Define performance baselines, SLAs, and acceptance thresholds for response time, throughput, error rate, and resource utilisation.
- Conduct bottleneck analysis using APM tools (Datadog, Dynatrace, New Relic, AppDynamics) and system-level profiling (CPU, memory, I/O, thread dumps, GC analysis).
- Execute scalability validation for microservices, APIs, and database layers under production-like traffic patterns.
- Integrate performance tests into CI/CD pipelines for continuous performance regression detection.
- Produce performance test reports with clear findings, recommendations, and risk assessments for stakeholders.
Quality Strategy & Collaboration
- Collaborate with cross-functional teams (developers, product, DevOps, SRE) to define test strategies, acceptance criteria, and quality goals.
- Analyse test results, identify root causes of failures, and drive resolution with development teams.
- Contribute to release planning and risk assessment by providing data-driven quality and performance insights.
- Mentor junior SDETs and QA engineers, promoting best practices in test design, automation, and performance engineering.
- Stay current with industry trends and emerging tools in test automation, performance engineering, and DevOps.
AI-Augmented Quality Engineering
- Leverage AI tools (GitHub Copilot, Cursor IDE, or equivalent) to accelerate test script development and maintenance.
- Explore and implement AI-assisted test case generation from requirements and user stories.
- Build or adopt AI-driven failure triage and root cause analysis workflows.
- Evaluate emerging AI testing tools and recommend adoption where they deliver measurable value
- Complete all assigned, mandatory training within the timeframe provided
- Conduct and/or participate in regularly scheduled 1:1 meetings with direct manager and/or direct reports
Skills & Technology Matrix
Category
Skills & Tools
Test Automation
Playwright (primary), Selenium WebDriver, REST Assured, PyTest, JUnit, TestNG, Cucumber/BDD
Programming
Java (strong), Python (strong), TypeScript, SQL
Performance Tools
JMeter, Gatling, k6, Locust, Artillery, Taurus, BlazeMeter
APM & Profiling
Datadog, Dynatrace, New Relic, AppDynamics, Grafana, JProfiler, VisualVM, async-profiler
Performance Analysis
Thread dump analysis, GC log analysis, heap profiling, CPU/memory/I-O bottleneck identification, database query profiling (EXPLAIN plans)
CI/CD & DevOps
Jenkins, GitHub Actions, GitLab CI, Docker, Kubernetes, Helm
API Testing
Postman, REST Assured, GraphQL testing, contract testing (Pact), gRPC testing
Cloud Platforms
AWS (EC2, ECS, EKS, CloudWatch, X-Ray), Azure (AKS, Monitor), GCP
AI Tooling
GitHub Copilot, Cursor IDE, AI-assisted test generation, LLM-based failure triage
Methodologies
Shift-left testing, TDD/BDD, risk-based testing, exploratory testing, chaos engineering principles
Monitoring & Observability
Prometheus, Grafana, ELK/EFK stack, CloudWatch, distributed tracing (Jaeger, Zipkin, OpenTelemetry)
Required Experience
Must-Haves
- 5+ years of experience in software engineering with a focus on test automation and quality.
- Strong software development fundamentals and production-grade coding skills in Java and/or Python.
- Hands-on test automation using Playwright (required) and Selenium.
- Proven experience across UI, API, and backend testing.
- CI/CD pipeline integration for automated testing (Jenkins, GitHub Actions, or equivalent).
- 2+ years of hands-on performance testing using JMeter, Gatling, k6, or Locust.
- Experience defining performance baselines, SLAs, and conducting bottleneck analysis with APM tools.
- Strong understanding of QA methodologies, test design patterns, and shift-left practices.
- AI-native mindset with demonstrated experience using AI tooling for test development.
- Experience mentoring junior engineers in test automation and performance engineering.
Nice-to-Haves
- Testing microservices and distributed systems at scale.
- Cloud platform experience (AWS, Azure, or GCP) for infrastructure-aware testing.
- Containerised environments (Docker, Kubernetes) for test environment management.
- Contract testing using Pact or similar frameworks.
- Chaos engineering experience (Gremlin, LitmusChaos, Chaos Monkey).
- Database performance tuning and query optimisation knowledge.
- AI-assisted test generation, predictive failure analysis, and intelligent test prioritisation.
- Experience with distributed tracing and observability for test diagnostics.
Mandate Skills (Per Intake Call)
Non-negotiable requirements confirmed during the hiring intake discussion:
#
Mandate Skill
What We’re Looking For
1
5+ years in Automation Testing
Proven track record across multiple projects/employers, not just one long stint
2
Selenium / Playwright
Playwright is primary; Selenium experience shows breadth. Must have architected, not just used.
3
Development in Java / Python
Production-grade code, not just test scripts. Comfortable with OOP, design patterns, and code reviews.
4
AI for Code / Test Cases
Active usage of Copilot, Cursor, or equivalent. Can describe specific workflow changes, not just tool names.
5
Performance & Load Testing
Hands-on with JMeter/Gatling/k6. Can design load profiles, analyse bottlenecks, and present findings to stakeholders.