JLL is a leading professional services firm that specializes in real estate and investment management, leveraging data-driven insights to create value for clients in a rapidly evolving market.

As a Data Engineer at JLL, you will play a crucial role in developing and maintaining the architecture that supports data processing and analysis across the organization. Key responsibilities include designing and implementing robust data pipelines, ensuring data quality and integrity, and collaborating with data scientists and analysts to provide actionable insights. You should possess strong skills in SQL, data warehousing, and cloud technologies such as Snowflake, as well as a deep understanding of data modeling and ETL processes. A successful candidate will not only have technical expertise but also a proactive attitude and the ability to communicate effectively with cross-functional teams, aligning with JLL's commitment to collaboration and innovation in the real estate sector.

This guide will help you prepare for your interview by providing insights into the specific skills and knowledge that JLL values in a Data Engineer, enabling you to demonstrate your fit for the position confidently.

The interview process for a Data Engineer position at JLL is structured to assess both technical skills and cultural fit within the organization. The process typically unfolds in several key stages:

1. Initial Contact

The journey begins with an initial contact from a recruiter, who will schedule a preliminary interview. This conversation is generally focused on your background, experience, and understanding of the data engineering field. The recruiter will also gauge your interest in JLL and discuss the company culture to ensure alignment.

2. Technical Screening

Following the initial contact, candidates usually undergo a technical screening, which may be conducted via video call. During this stage, you can expect questions that assess your proficiency in SQL and other relevant data technologies, such as Snowflake. The interviewer will likely present practical scenarios or problems to solve, allowing you to demonstrate your analytical skills and technical knowledge.

3. In-Depth Technical Interview

Candidates who successfully pass the technical screening will be invited to a more in-depth technical interview. This round often involves multiple interviewers and focuses on a broader range of topics, including data modeling, ETL processes, and possibly some machine learning concepts. Be prepared to answer questions that require you to think critically about data structures and algorithms, as well as to discuss your previous projects and experiences in detail.

4. Behavioral Interview

In addition to technical assessments, JLL places importance on cultural fit. Therefore, candidates will typically participate in a behavioral interview. This round aims to evaluate your soft skills, teamwork, and problem-solving abilities. Expect questions that explore how you handle challenges, work in teams, and align with JLL's values.

5. Final Interview

The final stage may involve a wrap-up interview with senior management or team leads. This conversation often revisits your technical capabilities and behavioral responses, while also providing an opportunity for you to ask questions about the team dynamics and future projects at JLL.

As you prepare for your interview, consider the types of questions that may arise in each of these stages.

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

Understand the Role and Its Requirements

Before your interview, take the time to thoroughly understand the responsibilities and expectations of a Data Engineer at JLL. Familiarize yourself with the tools and technologies commonly used in the role, such as SQL, Snowflake, and data pipeline frameworks. This will not only help you answer technical questions more effectively but also demonstrate your genuine interest in the position.

Prepare for Technical Questions

Given the emphasis on SQL and data-related questions in previous interviews, ensure you are well-prepared to tackle a variety of technical challenges. Brush up on your SQL skills, focusing on complex queries, joins, and data manipulation techniques. Additionally, be ready to discuss data modeling and ETL processes, as these are crucial components of a Data Engineer's role. Practice common SQL problems and be prepared to explain your thought process clearly.

Be Ready for Behavioral Questions

While technical skills are essential, JLL also values cultural fit and teamwork. Prepare for behavioral questions that assess your problem-solving abilities, collaboration skills, and adaptability. Use the STAR (Situation, Task, Action, Result) method to structure your responses, providing concrete examples from your past experiences that highlight your strengths and how you align with the company’s values.

Clarify Expectations

If you find that the interview questions are veering off into areas that seem unrelated to the Data Engineer role, don’t hesitate to ask for clarification. It’s important to ensure that both you and the interviewers are on the same page regarding the position's requirements. This can also demonstrate your proactive approach and willingness to engage in meaningful dialogue.

Follow Up Professionally

After your interview, send a thoughtful follow-up email thanking your interviewers for their time and reiterating your interest in the position. If you have not received feedback within a reasonable timeframe, consider sending a polite follow-up to inquire about the status of your application. This shows your enthusiasm for the role and keeps the lines of communication open.

Embrace the Company Culture

JLL values professionalism and collaboration, so be sure to convey your ability to work well in a team-oriented environment. Research the company culture and think about how your personal values align with JLL’s mission. This will help you articulate why you would be a great fit for the team and the organization as a whole.

By following these tips, you can approach your interview with confidence and a clear strategy, increasing your chances of success in securing a Data Engineer position at JLL. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at JLL. The interview will likely focus on your technical skills, particularly in data management, SQL, and data warehousing, as well as your understanding of machine learning concepts and their application in real-world scenarios.

Technical Skills

1. Can you explain the differences between SQL and NoSQL databases?

Understanding the distinctions between these database types is crucial for a Data Engineer, as it impacts data storage and retrieval strategies.

How to Answer

Discuss the fundamental differences in structure, scalability, and use cases for SQL and NoSQL databases, emphasizing when to use each type.

Example

“SQL databases are structured and use a predefined schema, making them ideal for complex queries and transactions. In contrast, NoSQL databases are more flexible, allowing for unstructured data storage, which is beneficial for applications requiring scalability and rapid development.”

2. Describe a complex SQL query you have written. What was its purpose?

This question assesses your practical experience with SQL and your ability to handle complex data manipulations.

How to Answer

Provide a specific example of a query you wrote, explaining the context, the data involved, and the outcome of your query.

Example

“I wrote a complex SQL query to analyze customer purchase patterns by joining multiple tables, including sales, customers, and products. The query helped identify trends that informed our marketing strategy, leading to a 15% increase in targeted campaign effectiveness.”

3. What is Snowflake, and how does it differ from traditional data warehouses?

Familiarity with modern data warehousing solutions like Snowflake is essential for a Data Engineer.

How to Answer

Explain the architecture of Snowflake and its advantages over traditional data warehouses, such as scalability and performance.

Example

“Snowflake is a cloud-based data warehousing solution that separates storage and compute, allowing for scalable and efficient data processing. Unlike traditional data warehouses, it can handle diverse data types and offers automatic scaling, which optimizes performance and cost.”

4. How do you ensure data quality and integrity in your data pipelines?

Data quality is critical in engineering roles, and interviewers want to know your strategies for maintaining it.

How to Answer

Discuss the methods you use to validate and clean data, as well as any tools or frameworks you employ to monitor data quality.

Example

“I implement data validation checks at various stages of the pipeline, using tools like Apache Airflow for orchestration. Additionally, I regularly conduct data audits and use automated testing to ensure data integrity before it reaches the end-users.”

Machine Learning Concepts

5. What machine learning algorithms would you consider for predicting house prices?

This question tests your understanding of machine learning applications relevant to the real estate domain.

How to Answer

Mention specific algorithms and explain why they are suitable for the task, considering factors like data types and feature selection.

Example

“I would consider using linear regression for its interpretability and efficiency with continuous variables. Additionally, decision trees or ensemble methods like Random Forest could be beneficial for capturing non-linear relationships in the data.”

6. How do you handle missing data in a dataset?

Handling missing data is a common challenge in data engineering and analysis.

How to Answer

Discuss various strategies for dealing with missing data, including imputation methods and the importance of understanding the context of the missing values.

Example

“I typically assess the extent and pattern of missing data before deciding on a strategy. For small amounts of missing data, I might use mean or median imputation, while for larger gaps, I consider using predictive modeling techniques to estimate missing values based on other features.”

7. Can you explain feature engineering and its importance in machine learning?

Feature engineering is a critical step in the machine learning process, and understanding it is vital for a Data Engineer.

How to Answer

Define feature engineering and discuss its role in improving model performance through the creation of relevant features.

Example

“Feature engineering involves transforming raw data into meaningful features that enhance model performance. It’s crucial because well-engineered features can significantly improve the accuracy of predictions, making the difference between a mediocre and a high-performing model.”

8. What tools or frameworks do you use for data processing and analysis?

This question assesses your familiarity with industry-standard tools and your ability to work with them effectively.

How to Answer

List the tools you have experience with and explain how you have used them in your previous projects.

Example

“I frequently use Apache Spark for large-scale data processing due to its speed and efficiency. For data analysis, I rely on Python libraries like Pandas and NumPy, which provide powerful data manipulation capabilities.”

• 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.

• What considerations should be made when testing hundreds of hypotheses with many t-tests? When conducting multiple t-tests, you need to consider the risk of Type I errors (false positives) due to multiple comparisons. Implement corrections like the Bonferroni correction or False Discovery Rate (FDR) to control for this risk.

• How does random forest generate the forest and why use it over logistic regression? Explain how random forest generates multiple decision trees and why it might be preferred over logistic regression for certain tasks.

• How do we handle missing square footage data in Seattle housing price model? You have 100K sold listings with 20% missing square footage data. Describe methods to handle this missing data to construct a predictive model.

• How would you combat overfitting in tree-based classification models? When training a classification model, explain strategies to prevent overfitting, particularly in tree-based models.

• Does increasing the number of trees in a random forest always improve accuracy? Discuss whether sequentially increasing the number of trees in a random forest model will continuously improve its accuracy.

• How to implement k-means clustering algorithm in Python from scratch? Given a two-dimensional NumPy array data_points, number of clusters k, and initial centroids, write a Python function to perform k-means clustering and return the cluster assignment for each data point.

• 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? You are building a model to predict home prices in a city and notice the prices are right-skewed. Should you take any action? If so, what steps should you take?

• Bonus: How should you handle a left-skewed target distribution? If the target distribution is heavily left-skewed, what actions should you take?

If you want more insights about the company, check out our main JLL Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about JLL’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every JLL Data Engineer interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!