Sr. SDET (TCF)

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.