RBC's data analyst interviews typically pair a recruiter phone screen with behavioral questions and experience-focused technical discussions; format and round count vary by team, and reported outcomes are mixed.
$90K
Avg. Base Comp
$110K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
RBC's data analyst interviews consistently emphasize real-world experience and clear communication over algorithmic testing. Across the reports reviewed, the process typically opens with a recruiter or HR phone screen focused on motivation (why RBC, career goals, and a walk-through of your background) before moving into one or more rounds that blend behavioral questions with a technical discussion.
Candidates describe being asked to walk through past projects, the technical approaches they took, and how they solved problems, rather than solving algorithmic problems live. One candidate was asked about their Python experience and ML techniques they had used at work, framed as a high-level discussion rather than a coding drill.
Format varies quite a bit by team and level. One Technical Systems Analyst Co-op candidate had a single 30-minute panel with two managers and two teammates. It included a question distinguishing a database from a datastore and a scenario about prioritizing competing tasks. An intern candidate described two 30-minute rounds with different levels of management. Another Analyst candidate received a final take-home assignment with no formal guidelines and no feedback afterward, so it pays to ask clarifying questions up front. A Summer Analyst report also mentioned live Python and SQL coding alongside derivatives and interest-rate questions; those details are specific to that markets-facing track rather than typical of general data analyst hiring.
Outcomes in the reports are mixed: one candidate received an offer after a single round, while others went through several stages without one. Interviewers also asked RBC-specific questions, such as what candidates knew about the bank's services and why they wanted to work at a financial institution. The most consistent thread is that interviewers want specific, concrete examples of past analytical work rather than hypothetical or textbook answers. Be ready to walk through what you actually built and how you approached it.
Synthesized from 13 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Rbc process.
I went through a pretty straightforward interview process for a Data Analyst role at RBC, and the biggest thing I noticed was that it leaned much more on experience and fit than on hard technical testing. After an HR call, I had an online interview with two staff members that lasted about 30 minutes. The atmosphere was relaxed, with a bit of small talk at the start, even about the weather, which helped take the edge off. They kept it conversational and asked standard behavioral questions, along with questions about my background in data analysis and my Python experience. There weren’t any specific coding exercises or deep technical problems in that round.
What stood out most was how much they wanted to hear about my previous work and whether it matched the role. In another part of the process, I was asked about the procedures I had followed in past roles and whether I had any related experience, which seemed to matter more than trying to test software problem-solving. I also got a question about ML techniques I had used at work, but it was more of a high-level discussion than a technical drill. The HR screen was also very typical: introduce yourself, explain what you know about the job, and talk through your short-term and long-term goals. Overall, the interviewers were friendly and the process felt smooth, but it was clear that having directly relevant experience was a big advantage. I didn’t get an offer, so my main takeaway is to be ready to clearly walk through your past data analysis work, explain your Python experience in practical terms, and connect your background to the role without expecting a heavy technical round.
Prep tip from this candidate
Be ready to talk through your past data analysis work and the procedures you followed, since the interviews focused on experience over coding. Also prepare a concise explanation of any ML techniques you’ve used at work, because that came up as a high-level technical question.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Rbc
Write a query to get the total three-day rolling average for deposits by day
| Question | |
|---|---|
| Size of Joins | |
| Hurdles In Data Projects | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| MLE for Default Prediction | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Employee Salaries | |
| Comments Histogram | |
| Top Three Salaries | |
| Closest SAT Scores | |
| Slacking Employees Salaries | |
| Last Transaction | |
| Find the Missing Number | |
| Monthly Customer Report | |
| Experiment Validity | |
| Payments Received | |
| Paired Products | |
| 500 Cards | |
| Total Spent on Products | |
| Maximum Profit | |
| Bagging vs Boosting | |
| Get Top N Frequent Words | |
| Top 3 Users | |
| Subscription Overlap | |
| Google Maps Improvement | |
| Button AB Test | |
| Department Expenses | |
| Prime to N |
Synthesized from candidate reports. Individual experiences may vary.
Most candidates report the process starts with a recruiter or HR phone screen, sometimes weeks after applying. This stage typically covers why you applied to RBC, a walk-through of your background, and your short- and long-term career goals; it is largely non-technical and designed to confirm basic interest and fit before moving forward.
Nearly every account describes at least one round built around behavioral questions such as 'tell me about yourself,' why you want the role, examples of teamwork, a past failure, or collaborating with someone whose working style differs from yours. Interviewers typically ask for specific past examples rather than general statements, and one candidate felt relevant experience and fit mattered more than technical testing.
Technical content varies by team rather than following one script. Most reports describe discussing past projects, Python experience, or technical approaches in a conversational format rather than live coding; one candidate was asked about ML techniques used at work at a high level. A Technical Systems Analyst Co-op candidate instead faced a question distinguishing a database from a datastore. A Summer Analyst report was the exception, with live Python and SQL coding, linear regression and data-cleaning questions, plus derivatives and interest-rate questions tied to that markets-facing track.
Some candidates describe a stage beyond the core interviews. One Analyst candidate received a final take-home assignment tied to the actual work, with no formal guidelines and no follow-up feedback. A Summer Analyst candidate attended a half-day assessment center that included a presentation. A Technical Systems Analyst Co-op candidate instead faced a single 30-minute panel with two managers and two team members. Because these steps differ by team, ask your recruiter early what format to expect.
Reported outcomes are mixed, with some candidates receiving offers and others not advancing after the final round. Some candidates mention unclear communication about where they stood between rounds, so it may help to ask recruiters directly about timeline and next steps rather than assuming updates will come automatically.
