Voya Financial Data Engineer Interview Questions + Guide in 2025

Voya Financial is committed to ensuring that everyone has the opportunity for a better financial future by providing innovative solutions and championing access to financial opportunities.

As a Data Engineer at Voya Financial, you will play a pivotal role in overseeing the design, development, and execution of data engineering solutions that align with the company’s mission. Your primary responsibilities will include building and optimizing data pipelines, implementing ETL processes, and ensuring data integrity and quality across various platforms. The ideal candidate will possess a strong technical background in cloud-based data technologies, particularly in Azure and Snowflake, along with proficiency in programming languages such as SQL, Python, or Java. You will be expected to collaborate closely with cross-functional teams, including data architects and analysts, to ensure that data solutions meet business requirements while adhering to Voya's commitment to diversity, equity, and inclusion.

This guide is designed to equip you with the insights and knowledge necessary to excel in your interview for the Data Engineer position at Voya Financial. With a focus on the company's values and expectations, you will be better prepared to demonstrate your fit for the role and contribute to their mission of fostering financial opportunities for all.

The interview process for a Data Engineer position at Voya Financial is structured to assess both technical skills and cultural fit within the organization. Candidates can expect a multi-step process that includes both technical and behavioral evaluations.

1. Phone Screening

The initial step typically involves a phone screening with one or more hiring managers. This conversation is designed to be more casual and conversational, allowing candidates to discuss their previous experiences and the specific projects they would be working on at Voya. During this call, candidates should be prepared to articulate their past roles, responsibilities, and any relevant artifacts they have created in previous projects.

2. Technical Interview

Following the phone screening, candidates will participate in a technical interview, which is conducted virtually. This interview usually lasts about an hour and focuses on assessing the candidate's technical expertise, particularly in SQL and data visualization tools like Tableau. Candidates may be asked to solve problems or answer questions related to data engineering concepts, such as SQL joins, ETL processes, and data modeling.

3. Behavioral Interview

The final round typically consists of a behavioral interview with a manager and possibly a senior analyst. This interview aims to evaluate the candidate's soft skills, teamwork, and alignment with Voya's values and culture. Candidates should be ready to discuss their approach to collaboration, problem-solving, and how they handle challenges in a team environment.

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

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

Understand Voya's Mission and Values

Voya Financial is deeply committed to creating opportunities for a better financial future for everyone. Familiarize yourself with their mission and values, and be prepared to discuss how your personal values align with theirs. This understanding will not only help you answer questions more effectively but also demonstrate your genuine interest in the company and its culture.

Prepare for Technical Assessments

Given the technical nature of the Data Engineer role, you can expect to face questions related to SQL, data modeling, ETL processes, and cloud technologies, particularly Azure and Snowflake. Brush up on your technical skills and be ready to solve problems on the spot. Practice explaining your thought process clearly, as communication is key in technical interviews.

Emphasize Collaboration and Teamwork

Voya values a team-oriented culture where collaboration is essential. Be prepared to share examples from your past experiences that highlight your ability to work effectively in teams, manage relationships, and drive projects to completion. Discuss how you have navigated challenges in team settings and contributed to achieving common goals.

Showcase Your Problem-Solving Skills

The ability to think critically and solve complex problems is crucial for a Data Engineer. Prepare to discuss specific instances where you identified a problem, analyzed data, and implemented a solution. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the impact of your actions.

Be Ready for Behavioral Questions

Expect behavioral interview questions that assess your fit within Voya's culture. Questions may revolve around how you handle conflict, adapt to change, or learn from failures. Reflect on your past experiences and be ready to share stories that demonstrate your resilience, adaptability, and commitment to continuous improvement.

Communicate Clearly and Effectively

Strong communication skills are essential for this role, especially when explaining complex technical concepts to non-technical stakeholders. Practice articulating your thoughts clearly and concisely. During the interview, ensure you listen actively and respond thoughtfully to questions, showing that you value the interviewers' insights.

Highlight Your Commitment to Diversity and Inclusion

Voya emphasizes the importance of diversity and inclusion in its workplace culture. Be prepared to discuss how you have contributed to fostering an inclusive environment in your previous roles. Share your thoughts on the value of diverse perspectives in problem-solving and innovation.

Ask Insightful Questions

Prepare thoughtful questions to ask your interviewers about the team dynamics, project priorities, and Voya's approach to data governance and quality. This not only shows your interest in the role but also helps you assess if Voya is the right fit for you.

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

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Voya Financial. The interview process will likely assess your technical skills, problem-solving abilities, and your capacity to work collaboratively within a team. Be prepared to discuss your previous experiences and how they relate to the responsibilities outlined in the job description.

Technical Skills

**1. Can you explain the different types of SQL joins and when to use each?

Understanding SQL joins is crucial for data manipulation and retrieval.**

How to Answer

Discuss the various types of joins (INNER, LEFT, RIGHT, FULL OUTER) and provide examples of scenarios where each would be applicable.

Example

“INNER JOIN is used when you want to retrieve records that have matching values in both tables. For instance, if I have a table of customers and a table of orders, an INNER JOIN would return only those customers who have placed orders. LEFT JOIN, on the other hand, would return all customers, including those who haven’t placed any orders, filling in NULLs for the order data.”

**2. Describe your experience with ETL processes.

ETL (Extract, Transform, Load) is a fundamental aspect of data engineering.**

How to Answer

Share specific examples of ETL processes you have designed or implemented, focusing on the tools and technologies used.

Example

“In my previous role, I developed an ETL pipeline using Apache NiFi to extract data from various sources, transform it using Python scripts for data cleaning, and load it into our Snowflake data warehouse. This process improved our data availability and reduced the time taken for reporting.”

**3. How do you ensure data quality and integrity in your projects?

Data quality is critical for reliable analytics and reporting.**

How to Answer

Discuss the methods and tools you use to validate and maintain data quality throughout the data lifecycle.

Example

“I implement data validation checks at multiple stages of the ETL process, using tools like Great Expectations to define expectations for data quality. Additionally, I regularly conduct audits and use automated testing to ensure that the data remains accurate and consistent.”

**4. What experience do you have with cloud-based data solutions, particularly Azure?

Familiarity with cloud technologies is essential for modern data engineering roles.**

How to Answer

Highlight your experience with Azure services and how you have utilized them in your projects.

Example

“I have extensive experience using Azure Data Factory for orchestrating data workflows and Azure SQL Database for data storage. In my last project, I migrated our on-premises data warehouse to Azure, which improved scalability and reduced costs significantly.”

**5. Can you explain the concept of Data Vault 2.0 and its advantages?

Data Vault 2.0 is a methodology for data warehousing that emphasizes agility and scalability.**

How to Answer

Provide a brief overview of Data Vault 2.0 and discuss its benefits in terms of data modeling and integration.

Example

“Data Vault 2.0 focuses on creating a scalable and flexible data model that can adapt to changing business needs. It separates raw data storage from business logic, allowing for easier integration of new data sources and improved historical tracking. This approach has helped my team reduce the time needed for data integration by 30%.”

Behavioral Questions

**1. Describe a challenging project you worked on and how you overcame obstacles.

This question assesses your problem-solving and teamwork skills.**

How to Answer

Choose a specific project, outline the challenges faced, and explain the steps you took to resolve them.

Example

“In a previous project, we faced significant delays due to data quality issues. I organized a series of workshops with stakeholders to identify the root causes and implemented a data governance framework. This not only resolved the immediate issues but also established a culture of accountability for data quality moving forward.”

**2. How do you prioritize tasks when managing multiple projects?

Effective prioritization is key in a fast-paced environment.**

How to Answer

Discuss your approach to task management and how you ensure deadlines are met.

Example

“I use a combination of Agile methodologies and project management tools like Jira to prioritize tasks based on urgency and impact. I hold regular check-ins with my team to reassess priorities and ensure we are aligned on our goals.”

**3. Can you give an example of how you have collaborated with non-technical stakeholders?

Collaboration is essential for successful project delivery.**

How to Answer

Share an experience where you effectively communicated technical concepts to non-technical team members.

Example

“I worked closely with the marketing team to understand their data needs for a campaign. I created visual dashboards using Tableau to present the data in an accessible way, which helped them make informed decisions and improved our campaign performance by 20%.”

**4. How do you handle feedback and criticism from peers or managers?

Receiving feedback is a part of professional growth.**

How to Answer

Discuss your openness to feedback and how you use it to improve your work.

Example

“I view feedback as an opportunity for growth. For instance, after receiving constructive criticism on my code quality, I took the initiative to enroll in a code review workshop, which not only improved my skills but also fostered better collaboration with my team.”

**5. What motivates you to work in data engineering?

Understanding your motivation can provide insight into your fit for the role.**

How to Answer

Share your passion for data and how it drives your work.

Example

“I am motivated by the potential of data to drive meaningful business decisions. The ability to transform raw data into actionable insights excites me, and I find great satisfaction in building systems that empower others to leverage data effectively.”