Bentley Systems is a leading infrastructure engineering software company dedicated to advancing global infrastructure through innovative software solutions.

As a Data Engineer at Bentley Systems, you will play a crucial role in delivering, maintaining, and evolving the company's enterprise data platform. Your key responsibilities will include designing and developing efficient data pipelines that serve various applications and stakeholders, ensuring data quality and reliability, and enhancing data processing workflows. You will be responsible for creating data models, documenting processes, and collaborating with both engineering and analytics teams to develop data products that meet business needs. A successful Data Engineer at Bentley will possess strong technical skills in data warehousing technologies, programming languages, and cloud services, as well as excellent problem-solving abilities and communication skills to engage effectively with both technical and non-technical stakeholders. The role aligns with Bentley's commitment to fostering a data-driven culture and empowering its employees through accessible and trusted data.

This guide will equip you with the insights needed to prepare for your interview, helping you understand the expectations and characteristics that will make you an excellent fit for the Data Engineer position at Bentley Systems.

The interview process for a Data Engineer role at Bentley Systems is structured to assess both technical expertise and cultural fit within the organization. Here’s what you can expect:

1. Initial Screening

The first step in the interview process is an initial screening, typically conducted via a phone call with a recruiter. This conversation lasts about 30 minutes and focuses on your background, skills, and motivations for applying to Bentley Systems. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role, ensuring that you have a clear understanding of what to expect.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment, which may be conducted through a video call. This assessment is designed to evaluate your proficiency in data engineering concepts, including data pipeline development, ETL processes, and database management. You may be asked to solve problems in real-time, demonstrating your ability to think critically and apply your technical knowledge effectively.

3. Behavioral Interview

After the technical assessment, candidates typically participate in a behavioral interview. This round focuses on your past experiences and how they align with Bentley's values and work culture. Expect questions that explore your teamwork, problem-solving abilities, and how you handle challenges in a collaborative environment. This is an opportunity to showcase your communication skills and your approach to working with both technical and non-technical stakeholders.

4. Onsite Interview (or Final Round)

The final stage of the interview process may involve an onsite interview or a comprehensive virtual interview, depending on the current circumstances. This round usually consists of multiple interviews with team members and managers. You will be assessed on your technical skills, problem-solving capabilities, and your fit within the team. This is also a chance for you to ask questions about the team dynamics, projects, and the company’s vision for data engineering.

5. Offer Discussion

If you successfully navigate the previous rounds, the final step will be a discussion regarding the job offer. This conversation will cover salary, benefits, and any other relevant details about your potential employment with Bentley Systems.

As you prepare for your interview, it’s essential to familiarize yourself with the types of questions that may arise during each stage of the process.

Here are some tips to help you excel in your interview.

Understand Bentley's Mission and Values

Familiarize yourself with Bentley Systems' mission to advance infrastructure engineering and their commitment to sustainability. This understanding will allow you to align your responses with the company's goals and demonstrate how your work as a Data Engineer can contribute to their vision. Be prepared to discuss how your values resonate with Bentley's focus on innovation and collaboration.

Highlight Your Technical Expertise

Given the emphasis on modern cloud tech stacks and data warehouse technologies, ensure you can articulate your experience with tools like Snowflake, Databricks, and Azure. Be ready to discuss specific projects where you designed and implemented data pipelines or data models. Providing concrete examples of your problem-solving skills and technical proficiency will set you apart.

Emphasize Collaboration and Communication Skills

As a Data Engineer, you will be working closely with both technical and non-technical stakeholders. Prepare to showcase your ability to gather business requirements and translate them into technical solutions. Highlight instances where you successfully collaborated with cross-functional teams, and be ready to discuss how you ensure clear communication and documentation throughout the data lifecycle.

Prepare for Scenario-Based Questions

Expect scenario-based questions that assess your problem-solving abilities and technical knowledge. Think through potential challenges you might face in data pipeline development or data quality assurance, and be prepared to discuss how you would approach these situations. Use the STAR (Situation, Task, Action, Result) method to structure your responses effectively.

Showcase Your Passion for Data

Demonstrate your enthusiasm for data and analytics by discussing any personal projects, contributions to open-source initiatives, or continuous learning efforts in the field. Bentley values a data-driven culture, so sharing your passion for empowering others with data can resonate well with the interviewers.

Be Ready to Discuss Data Security Practices

With the role involving data security policies and best practices, be prepared to discuss your experience with implementing security measures, such as role-based access control (RBAC) and data auditing. Understanding the importance of data integrity and security in a cloud environment will show that you are not only technically proficient but also responsible and forward-thinking.

Prepare Questions for Your Interviewers

Engage your interviewers by preparing thoughtful questions about the team dynamics, ongoing projects, and how the Data Engineering team contributes to Bentley's overall strategy. This shows your genuine interest in the role and helps you assess if the company culture aligns with your expectations.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Engineer role at Bentley Systems. Good luck!

Bentley Systems Data Engineer Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Bentley Systems. The interview will focus on your technical skills, problem-solving abilities, and your understanding of data engineering principles. Be prepared to discuss your experience with data pipelines, data modeling, and cloud technologies, as well as your ability to collaborate with cross-functional teams.

Technical Skills

1. Can you describe your experience with building data pipelines and the tools you have used?

This question aims to assess your hands-on experience with data pipeline construction and the technologies you are familiar with.

How to Answer

Discuss specific tools and frameworks you have used, such as Apache Airflow, Talend, or custom solutions. Highlight a project where you successfully built a data pipeline, focusing on the challenges you faced and how you overcame them.

Example

“I have built data pipelines using Apache Airflow and AWS Glue. In a recent project, I developed a pipeline that ingested data from multiple sources, transformed it using Python scripts, and loaded it into a Snowflake data warehouse. This project improved data availability for analytics by 30%.”

2. What strategies do you use to ensure data quality in your projects?

This question evaluates your understanding of data quality principles and practices.

How to Answer

Explain the methods you implement to maintain data integrity, such as validation checks, automated testing, and monitoring. Provide examples of how you have applied these strategies in past projects.

Example

“I implement data quality checks at various stages of the ETL process, including validation rules to catch anomalies. For instance, in a recent project, I set up automated tests that flagged any discrepancies in data formats, which helped us maintain a 99% accuracy rate in our datasets.”

3. Describe your experience with SQL and how you optimize queries for performance.

This question assesses your SQL proficiency and your ability to enhance database performance.

How to Answer

Discuss your experience with writing complex SQL queries and the techniques you use to optimize them, such as indexing, query restructuring, or using analytical functions.

Example

“I have extensive experience with SQL, particularly in optimizing queries for performance. In one project, I identified slow-running queries and implemented indexing strategies that reduced execution time by over 50%. I also utilized window functions to simplify complex aggregations.”

4. How do you approach data modeling for a new data warehouse?

This question focuses on your understanding of data modeling concepts and your approach to designing data structures.

How to Answer

Explain your process for gathering requirements, understanding source data, and designing the data model. Mention any specific methodologies you follow, such as star schema or snowflake schema.

Example

“When designing a data warehouse, I start by collaborating with stakeholders to gather requirements. I then analyze the source data to identify key entities and relationships, ultimately creating a star schema that optimizes query performance while ensuring data integrity.”

5. Can you explain the role of cloud technologies in your data engineering work?

This question evaluates your familiarity with cloud platforms and their application in data engineering.

How to Answer

Discuss your experience with cloud services, particularly those relevant to data engineering, such as Azure, AWS, or Google Cloud. Highlight specific projects where you utilized these technologies.

Example

“I have worked extensively with Azure for data engineering projects, utilizing Azure Data Factory for ETL processes and Azure SQL Database for storage. In a recent project, I migrated on-premises data to Azure, which improved scalability and reduced costs by 20%.”

Collaboration and Communication

1. Describe a time when you collaborated with non-technical stakeholders to gather data requirements.

This question assesses your ability to communicate effectively with diverse teams.

How to Answer

Share an example of a project where you worked with non-technical stakeholders, focusing on how you translated their needs into technical requirements.

Example

“In a project to develop a reporting dashboard, I collaborated with marketing teams to understand their data needs. I conducted workshops to gather requirements and translated them into technical specifications, ensuring the final product met their expectations.”

2. How do you document your data processes and workflows?

This question evaluates your approach to documentation and knowledge sharing.

How to Answer

Discuss the tools and methods you use for documentation, emphasizing the importance of clear and organized records for team collaboration.

Example

“I use Confluence for documenting data processes and workflows, ensuring that all team members have access to up-to-date information. I also create flowcharts and diagrams to visually represent complex processes, making it easier for others to understand.”

3. Can you give an example of how you handled a conflict within a team?

This question assesses your interpersonal skills and conflict resolution abilities.

How to Answer

Share a specific instance where you faced a conflict, explaining how you approached the situation and the outcome.

Example

“In a previous project, there was a disagreement between the data engineering and analytics teams regarding data definitions. I facilitated a meeting where both sides could express their concerns, leading to a collaborative agreement on standard definitions that improved our workflow.”

4. How do you ensure that your work aligns with the overall business goals?

This question evaluates your understanding of the business context in which you operate.

How to Answer

Discuss how you connect your technical work to business objectives, emphasizing the importance of understanding stakeholder needs.

Example

“I regularly engage with business stakeholders to understand their goals and challenges. By aligning my data engineering projects with these objectives, I ensure that the data products I develop provide actionable insights that drive business success.”

5. What role do you think data engineers play in fostering a data-driven culture?

This question assesses your perspective on the impact of data engineering on organizational culture.

How to Answer

Explain how data engineers contribute to a data-driven culture by providing reliable data and supporting self-service analytics.

Example

“Data engineers play a crucial role in fostering a data-driven culture by ensuring that data is accessible, reliable, and easy to analyze. By building robust data pipelines and collaborating with analytics teams, we empower users across the organization to make informed decisions based on data.”

• 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.

• Write a function to merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: Determine the time complexity.

• Create a function missing_number to find the missing number in an array. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

• 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 function to search for a target value in a rotated sorted array. Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. Write a function to search for a target value in the rotated array and return its index, or -1 if not found. Bonus: Achieve (O(\log n)) runtime complexity.

• Would you think there was anything fishy about the results of an A/B test with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you suspect any issues with these results?

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

• What would you do if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. What steps would you take to address this issue?

• Why would the number of job applicants decrease while job postings remain the same? You observe that the number of job postings per day has remained constant, but the number of applicants has been steadily decreasing. What could be the reasons for this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common problems in "messy" datasets.

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair based on this outcome.

• How would you write a function to calculate sample variance? Write a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places. For example, given test_list = [6, 7, 3, 9, 10, 15], the function should return 13.89.

• Is there anything suspicious about the A/B test results? Your manager ran an A/B test with 20 different variants and found one significant result. Evaluate if there is anything suspicious about these results.

• How would you find the median in (O(1)) time and space? Given a list of sorted integers where more than 50% of the list is the same repeating integer, write a function to return the median value in (O(1)) computational time and space. For example, given li = [1, 2, 2], the function should return 2.

• What are the drawbacks of the given data organization, and how would you reformat it? You have data on student test scores in two different layouts (dataset 1 and dataset 2). Identify the drawbacks of these layouts, suggest formatting changes to make the data more useful for analysis, and describe common problems seen in "messy" datasets.

• How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right choice, and how would you assess its performance before and after deployment?

• How does random forest generate the forest and why use it over logistic regression? Explain the process by which random forest generates its ensemble of trees. Additionally, discuss the advantages of using random forest compared to logistic regression.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. Describe scenarios where you would prefer a bagging algorithm over a boosting algorithm and discuss the tradeoffs between the two.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of this model and explain its predictions to non-technical stakeholders?

• What metrics would you use to track the accuracy and validity of a spam classifier? You are tasked with building a spam classifier for emails and have completed a V1 of the model. What metrics would you use to evaluate the model's accuracy and validity?

Considering a career as a Data Engineer at Bentley Systems? You're looking at the perfect opportunity to be part of a world-leading software company and work on cutting-edge cloud data solutions. Bentley Systems thrives on innovation, competitive salary, and unbeatable benefits within a supportive and collaborative environment. If you’re excited about solving complex problems and have a knack for data pipelines, warehousing, and SQL, this role could be your next big step.

For more insights into Bentley Systems, check out our main Bentley Systems Interview Guide. We’ve also compiled interview guides for other roles, where you can delve deeper into Bentley Systems' interview process for various positions.

At Interview Query, we equip you with the essential knowledge, confidence, and strategies to triumph in your Bentley Systems data engineer interview. You can explore our company interview guides for comprehensive preparation.

Good luck with your interview!