Recently, one of our clients—an international student with a technical background who had been studying in Canada for two years—secured a data engineering interview with RBC (Royal Bank of Canada) and successfully passed it with the support of our real-time remote interview assistance service. The interview question wasn’t a classic algorithm challenge, but it tested something deeper: engineering mindset, experience with data integration, and the ability to clearly communicate a solution. When this client came to us, they admitted they could write some SQL and had used Pandas a bit, but had no idea how to integrate multiple data sources or explain such a process clearly. It was also their only interview opportunity at the time.
So we took on the task. Throughout the entire interview, CSOAHELP provided invisible but vital support, acting like a silent partner to help the candidate navigate the challenge smoothly.
Here’s the actual interview question:
You are working for a retail company that wants to create a unified customer database from four data sources:
Database A: Contains customer purchase history with fields like CustomerID, ProductID, PurchaseDate. Database B: Stores customer demographic information with fields like CustomerID, Name, Email, Phone. Database C: Holds customer reviews and ratings with fields like CustomerID, ProductID, Rating, ReviewText. Data Source D: A .csv file that contains additional customer information with fields like CustomerID, MembershipLevel.
The goal is to create a single customer database for marketing and analytics. Describe how you would approach this task, including the steps you'd take, any transformations required, and the technologies/tools you'd use.
Additionally, provide an example of a challenge you might encounter during this integration process and how you would address it.
On the interview day, the candidate sat in front of their main device for the Zoom call with the RBC interviewer. Meanwhile, our support expert was silently monitoring from a secondary device, observing the video flow and tone of the conversation. Within the first minute after the interviewer presented the question, we pushed the following guidance into the assistant panel:
Start by talking about the ETL (Extract, Transform, Load) process. Explain that you would extract the data from each source, standardize field names and formats during the transformation step, and then load everything into a target database such as Snowflake or AWS Redshift. Then, go through how you would process each of the four data sources and explain that you'd use Python (Pandas) and SQL for pre-processing. Finally, emphasize that you would implement data quality checks and logging mechanisms.
The candidate repeated this structure almost word for word. Though a little nervous, the logic was sound, and the explanation came across as structured and clear. The interviewer clearly appreciated hearing terms like field mapping, data format standardization, and ETL workflows.
But the challenge didn’t end there.
Next, the interviewer followed up: “What if there are data conflicts—for example, two sources have different email addresses for the same Customer ID? How would you handle that?”
We immediately pushed a more advanced follow-up suggestion:
Say you’d prioritize data from the more reliable source—for example, consider Database B primary and the CSV secondary. You could also define rules, such as using the most recently updated record or validating email format to decide which to keep. Also mention that you’d log all data conflicts for later manual review.
The candidate took a breath, organized their thoughts, and responded almost exactly as prompted. They even added, “We could set up a small dashboard to monitor the conflict rate,” which made the answer sound even more thoughtful and practical. That comment clearly impressed the interviewer.
Then came another deeper question: “Tell me about a technical challenge you’re likely to face during this integration process, and how you would solve it.”
We had prepped two scenarios for this exact moment: one involving missing data and inconsistent formats, and another involving schema mismatches, redundant fields, and lack of unified naming conventions.
We sent the following suggestion:
Talk about schema inconsistencies. For example, Database A might use numeric CustomerIDs, while Database B might use strings with prefixes. Your solution would be to standardize the schema using a mapping table to unify the format. You’d also implement alias mapping to ensure no data is missed during joins.
The candidate ran with that answer and added a solid example: “In one case, we had one system using ‘UserID’ and another using ‘CustomerID.’ They meant the same thing, so we standardized them ahead of time and added comments for clarity.” That made the answer concrete and realistic, earning another approving nod from the interviewer.
Throughout the interview, the candidate never had to write a single complete line of code. When asked, “What tools would you use to integrate these sources?” we immediately pushed a final ready-made response:
I would use Python and Pandas to handle the CSV and semi-structured data, and SQL to pull from the databases. After cleaning, I’d load everything into Snowflake or Redshift, which support analytical queries in columnar storage. I’d design the process as a modular pipeline and use something like Airflow to orchestrate the ETL jobs.
The candidate smoothly paraphrased this. The interviewer clearly valued the understanding of the architecture more than specific syntax or code performance.
In the end, the candidate received an invitation to the second round. The interviewer’s exact words: “Your data engineering thinking is quite clear.” From the question design to the follow-up cadence, this interview wasn’t about tricky algorithms—it was about whether the candidate could handle real-world data integration and complexity.
Honestly, without our assistance, the candidate would likely have answered with something superficial like “I’d just join the tables with SQL.” But with CSOAHELP’s real-time support, it was like having a seasoned data architect by their side—suggesting answers, offering structure, filling in the gaps, and guiding decision-making. That’s what turned a difficult interview into a success.
If you ever face one of those deceptively “simple” interview questions that are actually filled with traps, don’t go it alone. It’s not about being smart or hardworking—it’s about having the right guidance when it counts. That’s what CSOAHELP’s remote live interview support is designed for.
We won’t lie for you or pretend to be you. But we will make sure you never blank out. We’ll push structural suggestions before you freeze. We’ll help you organize your response when your mind gets tangled. And we’ll prompt you to mention business value or technical best practices when you forget.
Most importantly, you won’t be fighting this alone.
If you have an upcoming data engineering interview with RBC, TD, BMO, Scotiabank, or any other tech or financial firm in North America or Europe, talk to us. We don’t just help you solve problems—we help you show your value in real-world scenarios.
Still think brute-force LeetCode grinding is the key to landing offers? Today’s top interviews reward clarity, communication, and the ability to adapt—more than memorized solutions. With CSOAHELP, you get to bring your best self forward, every time.
经过csoahelp的面试辅助，候选人获取了良好的面试表现。如果您需要面试辅助或面试代面服务，帮助您进入梦想中的大厂，请随时联系我。
If you need more interview support or interview proxy practice, feel free to contact us. We offer comprehensive interview support services to help you successfully land a job at your dream company.
