Senior Data Analyst - Data Products & Insights

As a Senior Data Analyst in the Data Products & Insights team at RBC, your main responsibilities include designing and implementing innovative data products that improve audit efficiency, conducting deep analysis of complex datasets, ensuring data integrity and quality, automating processes for scalability, and collaborating closely with audit teams to deliver actionable insights.

To qualify for the Senior Data Analyst role at RBC, candidates should have 5-7 years of relevant data analytics experience, strong skills in Python/R, SQL, and data visualization tools. A Bachelor's degree in Data Science, Statistics, Computer Science, or a related field is essential, along with excellent communication skills to convey technical concepts effectively.

The Senior Data Analyst at RBC supports the audit team by translating complex analytic findings into actionable insights, ensuring data quality during audit engagements, and collaborating on analytics solutions that meet the specific needs of the audit teams, thus enhancing overall audit effectiveness.

A Senior Data Analyst at RBC will leverage various tools and technologies, including advanced analytical software like Python/R, SQL for database management, and data visualization tools to transform raw data into meaningful insights that support audit processes.

RBC offers several professional growth opportunities for Senior Data Analysts, including mentorship programs, continuous training on emerging technologies and methodologies, and pathways for career advancement within a supportive and inclusive workplace that recognizes the value of diverse perspectives.

When answering this question, highlight specific tools you’ve used, like Tableau or Power BI. Discuss how you’ve applied these tools to present complex data in an understandable format to stakeholders, emphasizing your ability to convey insights effectively.

Use the STAR method to detail the situation, task, action, and result. Be specific about the dataset type and analysis techniques you employed, then explain how your insights influenced decision-making or audit processes.

Discuss your approach to data validation, your experience with testing data accuracy, and any tools or methodologies you utilize to maintain high data quality standards throughout your analysis.

Outline specific automation techniques you've implemented, like using Python scripts or SQL procedures, and discuss how these have reduced manual effort and improved efficiency in your previous roles.

Provide examples of past experiences where you successfully managed deadlines. Discuss your prioritization skills, ability to work under pressure, and any techniques you use to stay organized and focused.

Use the STAR method again to describe a successful collaboration experience. Highlight your role, what the challenge was, how you contributed to the team effort, and the outcome that benefited everyone.

Share your insights on technologies that are reshaping data analytics, such as machine learning or AI, and explain how you see their potential impact on the audit and analytics field, particularly at RBC.

Discuss your philosophy on mentorship, focusing on fostering skills and confidence. Give examples of techniques you use, such as hands-on training or regular feedback, to develop their analytical competencies.

Mention specific resources you use to stay informed, such as industry blogs, webinars, or professional groups. Explain how you apply this knowledge to benefit your work and enhance audit analytics at RBC.

Discuss specific scenarios where you've used analytical techniques to identify risks. Highlight tools or methodologies used in your analysis and how you communicated these findings to stakeholders for decision-making.