Senior QA Engineer (Contract)

Role Summary

The Senior QA Engineer owns quality across EGG Digital's web applications, backend services, and

data and AI-driven products. Working closely with Product Owners, developers, and data teams,

you define test strategy, build and maintain automated test frameworks, validate data integrity

directly at the source, and design test approaches for probabilistic AI features. You embed quality

gates into CI/CD, track and report quality metrics, and give the team a clear go or no-go view on

every release, always measuring quality against real business outcomes rather than just technical

correctness.

Roles and Responsibilities

• Test Strategy and Planning: Define the test strategy for features and releases, including risk-

based coverage, test pyramid balance, and entry and exit criteria, preparing test plans, cases,

and scripts that give comprehensive coverage against requirements and technical

specifications.

• Requirement Clarity & Business-Aligned Testing: Work closely with Product Owners, business

analysts, and data teams to understand why each feature exists and the business logic behind

it, crack open ambiguous or incomplete requirements early, and convert acceptance criteria

into concrete, testable scenarios before development starts, so that quality is measured

against real business outcomes.

• Test Execution: Perform end-to-end, integration, regression, smoke, and exploratory testing

across web applications and backend services, covering functional and non-functional

requirements including performance, security, and accessibility.

• Test Automation: Build and maintain automated test frameworks for web interfaces and APIs,

designing them for maintainability, parallel execution, and low flakiness, and extending

coverage over time to shorten release cycles and improve confidence.

• Data Validation: Validate data integrity by querying data stores directly, verifying that outputs,

calculations, aggregations, and pipeline results are correct, and building automated data-

quality checks for analytics features.

• AI Feature Testing: Define test approaches for AI-driven features where outputs are

probabilistic, using evaluation datasets, rubric-based scoring, tolerance thresholds, and

regression detection in partnership with AI engineers.

• Defect and Release Management: Identify, reproduce, document, and track defects through to

resolution, maintain quality metrics such as defect density, escape rate, automation coverage,

and flakiness, and provide a clear go or no-go view on each release.

• Quality Engineering and Process: Participate in requirement and design reviews to surface risk

and ensure testability early, integrate quality gates into CI/CD, contribute to performance and

load testing, and drive quality practice across the team.

Qualifications

• Educational Background: Bachelor's degree in Computer Science, Engineering, Information

Technology, or a related field.

• Experience: A minimum of 4 years in software quality assurance, with strong exposure to both

manual and automated testing of web applications and backend services, ideally in a product

or data-intensive environment.

• Methodology: Solid understanding of QA methodologies across functional, integration,

regression, performance, and security testing, with familiarity with shift-left practices, risk-

based testing, behaviour-driven development, and quality gates.

• Automation Proficiency: Hands-on experience with web and API test automation frameworks

(e.g. Playwright, Cypress, Selenium, REST Assured, Postman/Newman), and proficiency in at

least one language (e.g. TypeScript/JavaScript, Python, Java) sufficient to design, write, and

maintain automation frameworks.

• Performance and Non-Functional Testing: Experience with load and performance testing tools

(e.g. k6, JMeter, Locust), and awareness of security (OWASP Top 10) and accessibility (WCAG)

testing.

• Data Proficiency: Good working knowledge of SQL and relational databases, the ability to

validate data independently, and familiarity with data warehouses or lakehouses (e.g.

BigQuery, Databricks) and data pipeline testing.

• Business Curiosity and Product Collaboration: Willingness to learn the business logic and

domain behind what you test, work directly with product and business stakeholders, and

translate business rules into test scenarios. You want to understand why a feature exists, not

just how it works.

• Willingness to Learn AI: Curiosity about AI and an active desire to learn how to use Agentic AI

to test AI software and big data solutions. Hands-on exposure to AI-assisted testing tools, LLM-

based test generation, or agentic workflows is ideal, but you do not need to be an expert today

• Tooling and CI/CD: Experience with test management and defect tracking tools (e.g. Jira, Xray,

TestRail), Git, and integrating automated suites into CI pipelines (e.g. GitHub Actions, GitLab CI,

Jenkins), with Docker for test environments.

• AI-Assisted Testing: Practical use of AI tools for test case generation, test data synthesis, and

log analysis, with judgement about where human verification remains essential.

• Attention to Detail and Communication: Strong analytical and problem-solving skills, with the

persistence to isolate intermittent and data-dependent issues, and the ability to report quality

status clearly to developers, product, and data teams.

• A Plus: Experience building personalization and recommendation systems, CRM and loyalty

platforms, marketing technology (customer data platforms, campaign orchestration,

marketing automation), or social media and messaging platform integrations (e.g. LINE,

Facebook, TikTok). Experience in telecommunications, location or mobility data, or large-scale

data platform environments is also valued.