2027 Investor Services, PEY Data Analyst (12-16 months)

About the role

Working as a Data Analyst with RBC Investor Services, you will manage data across platforms and projects, define requirements, improve data quality, investigate exceptions, and support client needs. The 12–16-month Toronto student/co-op role suits candidates pursuing data, finance, business, economics, or related degrees with strong analytical, communication, and organizational skills.

Responsibilities

• Collaborate with product, business development, Client Ops, and digital teams to gather data requirements.
• Analyze legacy data channels, translate business rules, and define requirements for modern tools and channels.
• Lead agile-like, business-led initiatives across a matrix environment, including solution design, development, testing, and delivery.
• Build and enhance data-quality frameworks and operating models for measurement, monitoring, analysis, and resolution.
• Confirm critical data elements and quality rules; coordinate data flows and measurement points with data engineers.
• Monitor data-quality rules, investigate exceptions and escalations, provide weekly reports, and develop anomaly-detection solutions using AI and machine learning.

Program and Eligibility

• Toronto-based, full-time, salaried student/co-op position with 37.5 working hours per week.
• Student eligibility requires returning to school after the work term or needing the term for graduation.
• The role is associated with RBC’s Wealth Management platform and is a fixed-term student position.

Must-Have Requirements

• Working toward a bachelor’s or master’s degree in data, finance, business, economics, or a related field.
• Strong interpersonal skills and ability to work effectively independently and within a team.
• Professional written and verbal communication, with good analytical and problem-solving skills.
• Highly organized, detail-oriented, and accurate, with the ability to manage multiple priorities under pressure.
• Able to meet time-sensitive deadlines and think critically, including outside conventional approaches.

Nice-to-Have Requirements

• Financial services experience and understanding of the data lifecycle, data management, and data-quality practices.
• Programming experience with Python, Scala, Java, SQL, or similar languages.
• ETL and distributed computing experience with PySpark, Apache Spark, Hadoop, HDFS, or MapReduce.
• Experience with Kafka, PostgreSQL, AWS RDS, DynamoDB, Elasticsearch, or related database technologies.
• Cloud and infrastructure experience with AWS, Azure, Docker, Kubernetes, or Terraform.
• Experience with Jira, Confluence, Postman, APIs, Jenkins, Git, Power BI, or Tableau.