How to Prepare for Business Intelligence Interviews: Success Story

How to Prepare for Business Intelligence Interviews: Success Story


This is an Interview Query Success Story. Cheng Hui used Interview Query Premium to prepare for interviews at Google, Meta, and other top tech companies. We chatted with Cheng about her background, interview prep strategy, and how she landed the business


I studied math and financial mathematics as an undergraduate. My first job was in the finance industry, and I worked in that position for close to five years. I was performing data-related tasks, but not many tasks on the business intelligence side. Then, I accepted a job at Amazon as a business intelligence engineer.

What Was the Business Intelligence Engineer Role Like at Amazon?

That was my first real data analytics role even though I’d been performing related tasks previously but just didn’t have the same job title. However, the role opened doors, and I was able to advance in the field.

At Amazon, I worked on two different teams, and I was basically doing ad hoc analysis and a little bit of experimentation. But then I got pregnant, and I decided I wanted a change. That’s when I started interviewing and found Interview Query.

What Was the Interview Process Like? What Tools Did You Use?

I had been searching for an interview prep tool, which was when I saw a review online for Interview Query. I started by checking out several YouTube videos and mock interviews. I also tested the free version and really liked its flow, so I decided to upgrade to have access to all of the content as well as see solutions.

Initially, I thought: “OK, this will probably take more than half a year to a year to get a job.” But in reality, it took about a month and a half. I actually received three offers. I interviewed at Facebook, Google, and Convoy—a high-growth startup—and all three companies offered me a position.

What Factors Do You Think Led to Those Three Job Offers?

Previous experience is definitely important, especially on the soft skills side. One thing I will stress is that you need to be a strong communicator. Without the ability to communicate effectively, it doesn’t really matter how hard you study.

However, it’s also very important to have a sense of the actual questions that are asked in interviews. Each company has a different interview style. I had an idea of what Amazon would ask, but I had no visibility into what other companies would ask during interviews. Interview Query helped me understand differences in interview styles and personalize my interview prep for each company.

Interview Query was also mobile-friendly, so I could check questions on my phone while looking after a three-month-old when I couldn’t find time to open my laptop. I’d have her napping in my arm, literally one arm occupied, looking at problems with the other. I was able to prepare for all of the interviews from my phone.

Luckily for my job, since it’s business intelligence– and product analyst–related, SQL and Python weren’t asked about in interviews. However, SQL was pretty much bread and butter in my previous job, so I’d practice by doing the coding in my head.

What Was Your Process for Preparing for Interviews at a Startup?

Generally, there’s less interview prep information available for startups. But, luckily, Convoy was basically a spin-off company created by former Amazon leaders. The whole interview process is extremely similar to Amazon’s. It was some coding and one round of data analytics–type questions. The rest is all big questions.

What Advice Would You Give to New Grads Preparing for Data Science Interviews?

You have to work with real-world data. I oversaw an interview, and it’s clear to an interviewer how a candidate approaches a question and pays attention to the detail, whether they have experience or not with data. We call that data intuition, knowing what level of aggregation you need to get to, etc. The best way to do that is to work and play with actual data.

It’s common in interviews to be given a dataset and then has 10 problems related to that dataset. And often, the questions are engineered to make the level of aggregation not easy to see.

So practice the multi-step questions, the analytics case studies, and the take-home challenges. These questions ask, “We want to measure something. What metric would you choose?” Then, based on that metric, you’d be asked to pull the data and discuss specifics like scenarios in which it might fail, or if you don’t have an exact metric, what proxy can you use.

Know the advantages and disadvantages. In interviews, you have to prove your knowledge to the interviewer, and that’s sometimes hard to practice if you’re looking at one-off SQL questions.