CBRE is the world's largest commercial real estate services and investment firm, recognized for its comprehensive capabilities in managerial, technological, and advisory services. As part of CBRE’s Digital & Technology team, you'll be tackling complex challenges in a dynamic and innovative environment.
As a Software Engineer at CBRE, you'll employ your technical skills to build, modernize, and maintain scalable distributed applications using various cloud technologies and frameworks. You'll be contributing to the development of cutting-edge solutions that enhance organizational productivity and performance. Your role involves collaborating within Scrum teams, delving into cloud-native applications, and being at the forefront of technological advancements that drive CBRE's digital landscape.
In this guide, we’ll walk you through the interview process, highlight frequently asked questions, and provide valuable tips to help you succeed in your CBRE Software Engineer interview on Interview Query. Let's get started!
The first step is to submit a compelling application that reflects your technical skills and interest in joining CBRE as a Software Engineer. Carefully review the job description and tailor your CV according to the prerequisites.
Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.
If your CV is among the shortlisted few, a recruiter from the CBRE Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.
In some cases, the CBRE hiring manager may stay present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.
The whole recruiter call should take about 30 minutes.
Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the CBRE Software Engineer role usually uses virtual means, including video conference and screen sharing. Questions in this 1-hour-long interview stage may revolve around specific technologies listed in the job description, such as Java, Spring Boot, ReactJS, and cloud platforms.
In some cases, a take-home coding challenge might be provided. It's important to allocate sufficient time for this task as it can be work-intensive and often takes several hours to complete. Typical tasks revolve around data structures, problem-solving, and demonstrating full-stack capabilities.
Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. During your day at the CBRE office, multiple interview rounds, varying with the role, will be conducted. Your technical prowess, including programming and problem-solving capabilities, will be evaluated throughout these interviews.
If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Software Engineer role at CBRE.
The final rounds might include interviews with potential team members. These interviews can cover a mix of technical skill verification and cultural fit. Expect some technical discussions and also be prepared to answer situational and behavioral questions.
Quick Tips For CBRE Software Engineer Interviews
You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your CBRE interview include:
Typically, interviews at CBRE vary by role and team, but commonly Software Engineer interviews follow a fairly standardized process across these question topics.
How would you explain what a p-value is to someone who is not technical? Explain the concept of a p-value in simple terms to someone without a technical background.
How should you handle a right-skewed distribution when predicting real estate home prices? When building a model to predict real estate home prices, you notice the distribution is skewed to the right. Should you take any action, and if so, what should you do?
Bonus: How should you handle a left-skewed target distribution? If the target distribution is heavily left-skewed, what steps should you take?
Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.
Develop a function precision_recall
to calculate precision and recall metrics from a 2-D matrix.
Given a 2-D matrix P of predicted values and actual values, write a function precision_recall
to calculate precision and recall metrics. Return the ordered pair (precision, recall).
Write a SQL query to select the top 3 departments with at least ten employees and rank them by the percentage of employees making over 100K.
Given employees
and departments
tables, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.
Create a function traverse_count
to determine the number of paths in an (n \times n) grid.
Given an integer (n), write a function traverse_count
to determine the number of paths from the top left corner of an (n \times n) grid to the bottom right. You may only move right or down.
Develop a function is_subsequence
to check if one string is a subsequence of another.
Given two strings, string1
and string2
, write a function is_subsequence
to find out if string1
is a subsequence of string2
.
How does random forest generate the forest and why use it over logistic regression? Random forest generates a forest by creating multiple decision trees using bootstrapped subsets of the data and random subsets of features. It is often preferred over logistic regression for its ability to handle non-linear relationships and interactions between features.
How do we deal with missing square footage data to construct a housing price model? To build a model predicting housing prices in Seattle with 20% of listings missing square footage data, consider using imputation techniques such as mean/mode/median imputation, regression imputation, or more advanced methods like KNN imputation.
How would you combat overfitting when building tree-based models? To combat overfitting in tree-based models, use techniques such as pruning, setting a maximum depth, using a minimum number of samples per leaf, or employing ensemble methods like random forests or gradient boosting.
Will increasing the number of trees in a random forest always increase model accuracy? Increasing the number of trees in a random forest generally improves accuracy up to a point, after which the gains diminish and may plateau. It also increases computational cost, so a balance is needed.
How would you implement the k-means clustering algorithm in Python from scratch? Implement k-means clustering by initializing centroids, assigning data points to the nearest centroid, updating centroids based on the mean of assigned points, and repeating until convergence. Return the cluster assignments for each data point.
Average Base Salary
The interview process typically involves three to five stages, including a recruiter call, technical interviews, and a take-home coding challenge. Candidates might also interact with hiring managers, engineers, and team members. The technical rounds could cover a range of topics from software lifecycle, databases, frameworks, and coding proficiency in languages like Java, Python, and SQL.
Candidates need strong technical skills, including knowledge of Java, Spring Boot, ReactJS, AngularJS, NodeJS, and data architecture. Experience with cloud technologies (AWS/Azure), microservices, APIs, data profiling, and SQL are essential. Additionally, proficiency in CI/CD pipelines, Agile methodologies, and development tools like Git and Docker is highly valued.
As a Software Engineer at CBRE, you'll work on building cloud-native applications, modernizing legacy systems to scalable microservices architectures, and developing both web and mobile apps. Projects emphasize Agile methodologies and integrate DevOps culture, continuous security, integration, deployment, and monitoring.
CBRE values diversity and fosters an inclusive environment where integrity, service excellence, and innovation are crucial. The company encourages collaboration, learning, and growth, offering access to some of the industry's most talented engineers and designers.
Prepare by researching CBRE's products and services, reviewing common technical questions, and brushing up on relevant skills and technologies. Practicing with Interview Query can help you simulate interview scenarios and get familiar with the types of questions that might be asked. Additionally, be ready to discuss your past projects, problem-solving approaches, and how your experience aligns with CBRE's requirements.
If you're looking for insights on CBRE's interview process for the Software Engineer position, we've got you covered. The experiences shared by applicants vary widely, with some facing a highly rigorous technical evaluation, while others experienced a disorganized and unprofessional process. The journey through multiple rounds, including behavioral, technical, and coding challenges, highlights the need for thorough preparation.
To ensure you're fully prepared and can navigate all potential interview challenges, check out the CBRE Interview Guide on Interview Query, where we've gathered a collection of interview questions and tips specific to CBRE. For a more targeted preparation, explore our detailed guides for roles such as software engineer and data analyst. At Interview Query, we provide you with the tools, knowledge, and confidence you need to excel in your CBRE interviews.
Dive into our company interview guides to boost your preparation, and feel free to reach out with any questions.
Best of luck with your interview!