Workiva Data Engineer Interview Questions + Guide in 2025

Workiva is a leading provider of cloud-based solutions for financial and compliance reporting, empowering organizations to streamline their processes, increase transparency, and enhance collaboration.

As a Data Engineer at Workiva, you will play a pivotal role in designing, developing, and maintaining scalable data pipelines and systems that support analytics, machine learning, and business intelligence across the organization. Your key responsibilities will include building data ingestion pipelines, optimizing data transformations, and ensuring data quality and reliability. You will work with modern technologies such as Airbyte, Kafka, and Snowflake, and collaborate with cross-functional teams to enhance the self-service data platform.

To excel in this role, you will need extensive experience in data engineering, strong proficiency in SQL and Python, and a solid understanding of data modeling and performance optimization. Excellent problem-solving skills, a proactive approach to operational excellence, and the ability to communicate effectively with both technical and non-technical stakeholders are also crucial.

This guide will help you prepare for your interview by highlighting the skills and knowledge required for the role, as well as providing insights into the company culture and expectations.

The interview process for a Data Engineer at Workiva is structured to assess both technical skills and cultural fit, ensuring candidates align with the company's values and operational needs. The process typically unfolds in several stages:

1. Initial Phone Screening

The first step is a phone screening conducted by a recruiter. This conversation usually lasts around 30 minutes and focuses on your background, experience, and motivation for applying to Workiva. The recruiter will also gauge your understanding of the role and the company, as well as your alignment with Workiva's culture.

2. Technical Interview

Following the initial screening, candidates are invited to a technical interview, which may be conducted via video conferencing. This stage often includes coding exercises, such as pair programming or debugging tasks, where you will be asked to demonstrate your proficiency in SQL and Python. Expect to solve problems related to data ingestion, transformation, and performance optimization, reflecting the skills necessary for the role.

3. Behavioral Interview

After the technical assessment, candidates typically participate in a behavioral interview. This round may involve one-on-one discussions with team members or managers, focusing on your past experiences, problem-solving approaches, and how you handle team dynamics. Questions may explore scenarios where you faced challenges or disagreements within a team, as well as your strategies for collaboration and mentorship.

4. Panel Interview

In some cases, candidates may be invited to a panel interview, which includes multiple interviewers from different departments. This stage assesses your ability to communicate effectively with cross-functional teams and your understanding of the broader business context. You may be asked to present a case study or discuss a project you have worked on, showcasing your technical expertise and strategic thinking.

5. Final Assessment

The final stage may involve a more in-depth technical assessment or a presentation to the team. This could include discussing your approach to building scalable data pipelines or demonstrating your knowledge of data modeling and cloud technologies. Feedback is often provided promptly after this stage, allowing candidates to understand their performance and next steps.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that assess your technical skills and cultural fit.

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

Understand the Interview Process

The interview process at Workiva can be extensive, often involving multiple stages including phone screenings, technical interviews, and behavioral assessments. Be prepared for a mix of technical questions and discussions about your past experiences. Familiarize yourself with the structure of the interview process, as candidates have reported varying experiences, from straightforward to more complex multi-stage interviews. Knowing what to expect can help you manage your time and energy effectively.

Showcase Your Technical Skills

As a Data Engineer, your proficiency in SQL and Python will be crucial. Brush up on your SQL skills, focusing on query design and performance tuning, as well as your ability to build reusable libraries in Python. Expect to encounter technical challenges that may involve debugging or optimizing data pipelines. Practice coding problems that reflect real-world scenarios you might face in the role, such as building data ingestion pipelines or transforming data using tools like dbt.

Prepare for Behavioral Questions

Workiva places a strong emphasis on cultural fit and collaboration. Be ready to discuss your experiences working in teams, particularly how you handle disagreements or conflicts. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you highlight your problem-solving skills and ability to work collaboratively. Reflect on past experiences where you contributed to team success or improved processes, as these will resonate well with the interviewers.

Emphasize Your Problem-Solving Approach

Interviewers at Workiva are interested in your thought process as much as your technical skills. Be prepared to explain how you approach complex problems, particularly in data engineering contexts. Discuss specific examples where you identified issues, proposed solutions, and implemented changes. This will demonstrate your analytical skills and your ability to deliver high-quality, reliable solutions.

Engage with the Interviewers

During your interviews, take the opportunity to engage with your interviewers. Ask insightful questions about the team dynamics, the technologies they use, and the challenges they face. This not only shows your interest in the role but also helps you assess if Workiva is the right fit for you. Remember, interviews are a two-way street, and demonstrating curiosity can leave a positive impression.

Be Adaptable and Open to Feedback

Candidates have noted that the interviewers at Workiva are approachable and respectful. Be open to feedback during technical assessments, and don’t hesitate to ask clarifying questions if you’re unsure about a problem. This shows your willingness to learn and adapt, which is a valuable trait in a collaborative environment.

Follow Up Professionally

After your interviews, consider sending a thank-you email to express your appreciation for the opportunity to interview. This is a chance to reiterate your interest in the role and reflect on a specific topic discussed during the interview. A thoughtful follow-up can help you stand out in a competitive hiring process.

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

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Workiva. The interview process will likely focus on your technical skills, problem-solving abilities, and cultural fit within the organization. Be prepared to discuss your experience with data pipelines, SQL, and cloud technologies, as well as your approach to collaboration and operational excellence.

Technical Skills

1. Can you describe your experience with building data ingestion pipelines?

This question aims to assess your hands-on experience with data ingestion tools and techniques.

How to Answer

Discuss specific tools you have used (like Airbyte or Kafka) and the types of data sources you have worked with. Highlight any challenges you faced and how you overcame them.

Example

“I have built data ingestion pipelines using Kafka to stream data from various sources, including APIs and databases. One challenge I faced was ensuring data consistency during high traffic periods, which I addressed by implementing a buffering mechanism that allowed for smoother data flow.”

2. How do you optimize SQL queries for performance?

This question evaluates your understanding of SQL and your ability to enhance query efficiency.

How to Answer

Explain your approach to query optimization, including indexing, query structure, and analyzing execution plans.

Example

“I optimize SQL queries by first analyzing the execution plan to identify bottlenecks. I often use indexing on frequently queried columns and rewrite complex joins to reduce the overall execution time. For instance, I improved a slow-running report by restructuring the query and adding appropriate indexes, which reduced the runtime by over 50%.”

3. What is your experience with cloud technologies, particularly AWS?

This question assesses your familiarity with cloud platforms and their services.

How to Answer

Mention specific AWS services you have used, such as S3, Redshift, or Lambda, and describe how you utilized them in your projects.

Example

“I have extensive experience with AWS, particularly with S3 for data storage and Redshift for data warehousing. In my last project, I set up a data pipeline that ingested data into S3 and then transformed and loaded it into Redshift for analytics, which significantly improved our reporting capabilities.”

4. Can you explain the concept of a Data Lake and its advantages?

This question tests your knowledge of data architecture and storage solutions.

How to Answer

Define a Data Lake and discuss its benefits, such as scalability and flexibility in handling various data types.

Example

“A Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. The main advantage is its ability to handle diverse data types, which enables organizations to perform advanced analytics and machine learning without the need for extensive data transformation upfront.”

5. Describe a time when you had to troubleshoot a production issue.

This question evaluates your problem-solving skills and ability to handle real-world challenges.

How to Answer

Provide a specific example of a production issue you encountered, the steps you took to diagnose and resolve it, and the outcome.

Example

“Once, we experienced a significant delay in our data processing pipeline. I quickly identified that a downstream service was failing due to a schema change. I coordinated with the team to roll back the change and implemented a monitoring alert to catch similar issues in the future, which improved our response time significantly.”

Behavioral Questions

1. Talk about a time when you disagreed with a team member.

This question assesses your interpersonal skills and ability to work collaboratively.

How to Answer

Describe the situation, your perspective, and how you resolved the disagreement while maintaining a positive working relationship.

Example

“I once disagreed with a colleague on the approach to a data transformation process. I suggested we hold a meeting to discuss our viewpoints and gather input from the rest of the team. This collaborative approach not only resolved the disagreement but also led to a more robust solution that incorporated the best of both ideas.”

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

This question evaluates your organizational skills and ability to manage time effectively.

How to Answer

Discuss your method for prioritizing tasks, such as using project management tools or frameworks like Agile.

Example

“I prioritize my tasks by assessing deadlines and the impact of each project. I use tools like Jira to track progress and ensure that I’m focusing on high-impact tasks first. For instance, during a recent project, I prioritized critical data pipeline updates that were essential for a client deliverable over less urgent tasks.”

3. Describe a project where you had to collaborate with cross-functional teams.

This question assesses your teamwork and communication skills.

How to Answer

Provide an example of a project that required collaboration, detailing your role and how you facilitated communication among teams.

Example

“In a recent project, I collaborated with data scientists and application engineers to develop a new analytics feature. I organized regular check-ins to ensure alignment and shared updates through a centralized communication platform, which helped us stay on track and meet our deadlines.”

4. What motivates you to work in data engineering?

This question aims to understand your passion for the field and your long-term career goals.

How to Answer

Share what excites you about data engineering, such as solving complex problems or the impact of data on business decisions.

Example

“I am motivated by the challenge of transforming raw data into actionable insights. I find it rewarding to build systems that enable teams to make data-driven decisions, and I am excited about the potential of AI and machine learning to further enhance our capabilities.”

5. How do you stay updated with the latest trends in data engineering?

This question evaluates your commitment to professional development and staying current in the field.

How to Answer

Mention specific resources you use, such as online courses, industry blogs, or conferences.

Example

“I stay updated by following industry blogs like Towards Data Science and participating in webinars and online courses. I also attend conferences whenever possible to network with other professionals and learn about emerging technologies and best practices in data engineering.”