Student, Quality Engineering Analyst (Winter 2027)

Join the Quality Engineering Analytics (QEA) team and help turn Quality Engineering data into useful insight, automation and trusted AI-enabled capabilities.

You will work with engineers and stakeholders on QEA 360 data pipelines, dashboards, data-quality controls and agentic AI use cases that help teams understand quality, risk, adoption and delivery outcomes.

This is a hands-on learning role for a student who is curious about data, analytics, Quality Engineering and how Agentic AI can safely support real engineering workflows.

What will you do?

• Support QEA 360 data and analytics: Help ingest, validate and analyze Quality Engineering data from sources such as qTest, Jira, delivery pipelines and operational systems. Contribute to reliable data pipelines and well-defined metrics.

• Improve data quality and automated testing: Assist with data-integrity checks, pipeline testing, dashboard validation, and automation that improves the accuracy, reliability and supportability of QEA products.

• Contribute to agentic AI use cases: Support proofs of concept and product experiments where AI agents analyze quality data, assist with defect and risk insights, or help automate multi-step Quality Engineering workflows.

• Evaluate AI-generated outcomes: Help define test scenarios, expected evidence and quality checks for agent outputs. Review results for accuracy, usefulness, traceability, and appropriate human oversight.

• Build analytics and visualizations: Use dashboards, reporting tools and clear data storytelling to help clients and leaders understand trends, platform usage, AI adoption and opportunities for improvement.

• Research emerging practices: Explore developments in agentic AI, AI assurance, analytics and Quality Engineering, then summarize practical implications and recommendations for the team.

• Work across teams: Collaborate with Quality Engineering, Enterprise DevOps, AI enablement teams and platform partners to deliver small yet useful increments of work.

• Document and share what you learn: Create clear technical notes, user guidance and demonstrations so that experiments and improvements can be reused and supported.

What do you need to succeed?

• Currently enrolled in a post-secondary program in Computer Science, Software Engineering, Data Science, Data Analytics, Information Systems, Business Technology Management, or a related field with a graduation date of April 2027 or later

• Demonstrated interest in data, software engineering, Quality Engineering, automation or AI-enabled product development.

• Foundational programming or scripting skills in Python, Java, JavaScript, or a similar language.

• Foundational knowledge of SQL, data structures, APIs, or database concepts.

• Ability to analyze a problem, test assumptions, and explain findings clearly.

• Curiosity about agentic AI, including how agents reason over context, use tools, execute workflows and produce evidence that people can evaluate.

• Comfort learning unfamiliar tools and technologies with support from experienced team members.

• Clear written and verbal communication skills and the ability to work collaboratively in a team environment.

• Attention to detail and a responsible approach to data, privacy, security and AI use.

Preferred skills and experience

• Hands-on experience from coursework, personal projects, hackathons or prior work using Python and SQL.

• Exposure cloud services, especially AWS data or AI services such as S3, Glue, Redshift, Lambda, SageMaker or Amazon Bedrock.

• Exposure to AI agents, large language models, prompt design, retrieval-augmented generation, tool use or workflow orchestration.

• Experience with data visualization or business intelligence tools such as QuickSight, Power BI or Grafana.

• Familiarity with automated testing, CI/CD, Git, APIs or Agile product delivery.

• An understanding of responsible AI concepts such as evaluation, guardrails, explainability, traceability, and human oversight.

• Experience turning technical analysis into a concise presentation, demo or recommendation.

What is in it for you?

• Practical experience with enterprise Quality Engineering data, analytics and AI-enabled workflows.

• Exposure to current agentic AI work, with an emphasis on evaluation, trust and responsible use rather than AI experimentation alone.

• Opportunities to contribute real code, analysis, testing, dashboards and documentation with coaching from experienced engineers and analysts.

• A collaborative environment where students are encouraged to ask questions, test ideas, and demonstrate what they have learned.

• Experience working across engineering, data, analytics, platforms and client teams.

• A flexible and inclusive workplace focused on continuous learning and meaningful outcomes.