Willis Towers Watson Data Engineer Interview Questions + Guide in 2025

Willis Towers Watson is a global leader in professional services, providing data-driven solutions to help organizations manage risk, optimize benefits, and cultivate talent.

As a Data Engineer at Willis Towers Watson, you will play a critical role in designing and implementing robust data pipelines and ETL processes that support the company’s commitment to innovation and excellence. Your key responsibilities will include developing high-throughput data processing frameworks using technologies such as Spark and Databricks, collaborating with cross-functional teams to gather requirements and analyze data, and ensuring compliance with architectural standards to create a flexible and scalable data environment. A strong proficiency in Python for data manipulation and transformation is essential, alongside hands-on experience with tools like Apache Airflow and Azure Data Factory for orchestrating data workflows. You will also engage in DevOps activities to deploy Spark jobs and mentor junior team members.

Successful candidates will embody the company’s values of collaboration, integrity, and excellence, and possess strong problem-solving skills, excellent communication abilities, and a dedication to staying updated on industry trends. This guide will help you prepare for the interview by highlighting the specific skills and experiences that are crucial for this role and aligning them with the company’s mission and values.

The interview process for a Data Engineer position at Willis Towers Watson is structured and thorough, designed to assess both technical skills and cultural fit. The process typically unfolds in several stages:

1. Initial Screening

The first step is an initial phone interview, usually lasting around 30 minutes. This conversation is typically conducted by a recruiter who will provide an overview of the company and the role. During this call, candidates can expect to discuss their background, experiences, and motivations for applying, as well as answer some preliminary questions to gauge their fit for the company culture.

2. Technical Assessment

Following the initial screening, candidates may be required to complete a technical assessment. This could involve a coding task or a take-home project that tests their proficiency in relevant technologies such as Python, Spark, and ETL processes. The assessment is designed to evaluate the candidate's problem-solving abilities and technical knowledge in a practical context.

3. Behavioral Interview

Candidates who successfully pass the technical assessment will typically move on to a behavioral interview. This round often employs the STAR (Situation, Task, Action, Result) method to explore past experiences and how candidates have handled various challenges. Questions may focus on teamwork, conflict resolution, and project management, allowing interviewers to assess interpersonal skills and cultural fit.

4. Final Interview

The final stage usually consists of a more in-depth interview with hiring managers or team leads. This round may include a mix of technical and situational questions, as well as discussions about the candidate's previous work and how it relates to the role at Willis Towers Watson. Candidates may also be asked to present their approach to a specific project or problem relevant to the position.

5. Offer and Negotiation

If all goes well, candidates will receive an offer, which may be followed by a negotiation phase regarding salary and benefits. Throughout the process, candidates are encouraged to ask questions about the company, team dynamics, and expectations for the role.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and past experiences.

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

Understand the Company Culture

Willis Towers Watson values innovation, collaboration, and a commitment to excellence. Familiarize yourself with their mission and recent projects, especially those related to data-driven solutions. This knowledge will not only help you answer questions more effectively but also demonstrate your genuine interest in the company.

Prepare for Behavioral Questions

Expect a significant focus on behavioral questions that assess your personality and how you handle various situations. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Be ready to discuss times when you faced challenges, worked with difficult team members, or had to adapt to unexpected changes. This will showcase your problem-solving skills and ability to work in a team-oriented environment.

Highlight Technical Proficiency

Given the emphasis on technical skills such as SQL, algorithms, and Python, ensure you can discuss your experience with these technologies in detail. Be prepared to explain your approach to designing data pipelines, optimizing queries, and implementing ETL processes. If you have experience with Databricks or Spark, be ready to share specific examples of how you utilized these tools in past projects.

Showcase Your Project Experience

During the interview, you may be asked to walk through specific projects you've led or contributed to. Prepare to discuss the challenges you faced, the solutions you implemented, and the outcomes of your efforts. This not only demonstrates your technical skills but also your ability to manage projects and collaborate with others.

Be Ready for Technical Assessments

Expect a technical assessment as part of the interview process. This may include coding challenges or problem-solving scenarios related to data engineering. Practice coding problems that involve algorithms and data structures, as well as SQL queries. Familiarize yourself with common data manipulation tasks in Python, as these may come up during the assessment.

Communicate Clearly and Confidently

Effective communication is key in any interview. Practice articulating your thoughts clearly and concisely. Be prepared to explain complex technical concepts in a way that is understandable to non-technical stakeholders. This will demonstrate your ability to bridge the gap between technical and business teams.

Follow Up and Reflect

After your interviews, take the time to reflect on your performance and follow up with a thank-you note. Express your appreciation for the opportunity to interview and reiterate your interest in the role. This not only shows professionalism but also keeps you top of mind for the hiring team.

By preparing thoroughly and approaching the interview with confidence, you can position yourself as a strong candidate for the Data Engineer role at Willis Towers Watson. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Willis Towers Watson. The interview process will likely assess your technical skills in data engineering, particularly with ETL processes, Spark, and Python, as well as your problem-solving abilities and cultural fit within the team. Be prepared to discuss your past experiences and how they relate to the responsibilities outlined in the job description.

Technical Skills

1. Can you explain the ETL process and how you have implemented it in your previous projects?

Understanding the ETL process is crucial for a Data Engineer, as it forms the backbone of data integration and transformation.

How to Answer

Discuss your experience with ETL tools and frameworks, emphasizing specific projects where you designed or implemented ETL processes. Highlight any challenges you faced and how you overcame them.

Example

“In my previous role, I designed an ETL pipeline using Apache Airflow to automate data extraction from various sources, transform it using Python scripts, and load it into a data warehouse. One challenge was ensuring data quality, which I addressed by implementing validation checks at each stage of the pipeline.”

2. Describe your experience with Spark and how you have used it in data processing.

Spark is a key technology for data processing in this role, so demonstrating your expertise is essential.

How to Answer

Provide specific examples of how you have utilized Spark for data processing tasks, including any performance optimizations you implemented.

Example

“I have used Spark extensively for processing large datasets, particularly in a project where I optimized a batch processing job that reduced execution time by 30% by leveraging Spark’s partitioning and caching features.”

3. How do you optimize Spark jobs for performance?

Performance optimization is critical in data engineering, especially when dealing with large datasets.

How to Answer

Discuss techniques you have used to optimize Spark jobs, such as tuning configurations, using efficient data formats, or optimizing transformations.

Example

“I optimize Spark jobs by carefully selecting the right data format, such as Parquet for its columnar storage benefits, and by tuning Spark configurations like executor memory and parallelism based on the workload. Additionally, I analyze the execution plan to identify bottlenecks.”

4. Can you explain how you would implement a data pipeline using Databricks?

Databricks is a significant part of the technology stack, so familiarity with it is important.

How to Answer

Outline the steps you would take to set up a data pipeline in Databricks, including data ingestion, processing, and output.

Example

“To implement a data pipeline in Databricks, I would start by using Delta Lake for data ingestion to ensure ACID transactions. Then, I would create notebooks for data transformation using Spark SQL and Python, and finally, I would schedule the pipeline using Databricks Jobs for regular execution.”

Behavioral Questions

5. Describe a time when you faced a significant challenge in a project. How did you handle it?

This question assesses your problem-solving skills and resilience.

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure your response, focusing on the challenge and your approach to resolving it.

Example

“In a previous project, we faced a major data quality issue that threatened our deadlines. I organized a team meeting to identify the root cause, which turned out to be a data source inconsistency. We implemented a temporary fix while I worked on a long-term solution, which ultimately improved our data validation processes.”

6. How do you prioritize tasks when working on multiple projects?

Time management and prioritization are key skills for a Data Engineer.

How to Answer

Discuss your approach to prioritizing tasks, including any tools or methodologies you use.

Example

“I prioritize tasks based on project deadlines and impact. I use tools like Trello to visualize my workload and ensure that I’m focusing on high-impact tasks first. Regular check-ins with my team also help me adjust priorities as needed.”

7. Can you give an example of how you have worked effectively in a team?

Collaboration is essential in data engineering roles, so demonstrating your teamwork skills is important.

How to Answer

Share a specific example of a successful team project, highlighting your role and contributions.

Example

“I worked on a cross-functional team to develop a new data analytics platform. My role involved collaborating with data scientists to understand their requirements and translating them into technical specifications. This collaboration led to a successful launch that met all user needs.”

8. Why do you want to work at Willis Towers Watson?

This question assesses your motivation and fit for the company culture.

How to Answer

Research the company’s values and mission, and relate them to your own career goals and values.

Example

“I admire Willis Towers Watson’s commitment to innovation and excellence in data-driven solutions. I am excited about the opportunity to contribute to a company that values data as a strategic asset and fosters a collaborative environment.”