Student, Quality Engineering Analyst

At Sun Life,we’redriven by our Purpose: helping our Clientsachieve lifetime financial security and live healthier lives. Our values shape how we work: caring, authentic, bold, inspiring, and impactful.

Our student and new graduate programs offer a chance to explore Sun Life from the inside. You’ll make a difference in the lives of our customers and communities, while building the skills and experience that allow you to grow in a meaningful and rewarding way.

Job Description

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 to 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