QA Engineer – Data, DevOps & AI

We are seeking a QA Engineer with strong data-testing and automation capabilities to join the Corporate Data Cloud team. You will assure the quality and reliability of cloud-based data and AI solutions through data validation, test automation, CI/CD integration, and foundational testing of Generative AI and Agentic AI applications.

Key Responsibilities

• Design and execute test scenarios and test cases based on business and functional requirements.
• Validate end-to-end ETL/ELT pipelines, data transformations, integrations, and business rules.
• Perform data quality, reconciliation, regression, integration, and large-volume data testing.
• Develop and maintain automated test frameworks using Python, PyTest, Cucumber, or equivalent tools.
• Use advanced SQL and Snowflake to validate data and perform root-cause analysis.
• Integrate automated tests and quality gates into CI/CD pipelines.
• Support Git-based development practices, deployment validation, configuration checks, log analysis, and release verification.
• Test Generative AI solutions for accuracy, relevance, groundedness, consistency, hallucination risk, and guardrail effectiveness.
• Validate Agentic AI workflows, including tool and API interactions, multi-step execution, error handling, retry behaviour, and task completion.
• Create reusable test datasets and automated evaluation checks for data and AI solutions.
• Use AI-assisted approaches to improve test-case generation, automation, defect analysis, and QA efficiency.
• Identify, document, track, and communicate defects, risks, and quality metrics.
• Collaborate with cross-functional teams to improve engineering quality and release outcomes.

Required Skills and Experience

• 3–7 years of experience in Data QA, ETL testing, data quality engineering, or test automation.
• Strong SQL skills, including complex queries, joins, aggregations, window functions, and data reconciliation.
• Hands-on experience with Snowflake and data warehouse or data-platform testing.
• Experience in Python-based test automation using PyTest or a similar framework.
• Working knowledge of CI/CD tools such as Azure DevOps, GitHub Actions, Jenkins, or equivalent.
• Understanding of Git, branching, pull requests, deployment workflows, logging, and monitoring.
• Familiarity with AWS and/or Azure and modern cloud-based data architectures.
• Basic understanding of Generative AI concepts, including prompts, grounding, hallucinations, non-deterministic outputs, and guardrails.
• Basic understanding of Agentic AI concepts, including agents, tools, APIs, orchestration, and multi-step workflows.
• Experience working in Agile delivery environments using Jira or an equivalent work-management tool.
• Strong analytical, troubleshooting, communication, and collaboration skills.

Benefits LSEG offers a range of tailored benefits and support, including healthcare, retirement planning, paid volunteering days, and wellbeing initiatives.