Verily Data Engineer Interview Questions + Guide in 2025

Verily, a subsidiary of Alphabet, is pioneering a data-driven approach to transform healthcare management and delivery, aiming to make precision health accessible to everyone every day.

As a Data Engineer at Verily, you will leverage your expertise in data architecture and healthcare informatics to design and develop advanced data solutions that enhance usability and facilitate informed decision-making. Your role will involve collaborating with cross-functional teams to build robust data architectures that support the Precision Health Platform, ensuring compliance with industry standards. You will be responsible for transforming healthcare data into FHIR-compliant representations and implementing strategies for the integration of diverse healthcare data sources. Your skills in SQL, Python, and cloud computing will be critical, while your ability to mentor team members and thrive in a fast-paced environment will make you an invaluable asset to the organization.

This guide will help you prepare effectively for your job interview by providing insights into the key responsibilities and skills required for the Data Engineer role at Verily.

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

1. Initial Screening

The process begins with an initial screening, typically conducted by a recruiter over a phone call. This conversation lasts about 30 minutes and focuses on your background, skills, and motivations for applying to Verily. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role, ensuring that you understand the expectations and responsibilities.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment, which may be conducted via a video call. This assessment is designed to evaluate your proficiency in data modeling, integration, and relevant programming languages such as SQL and Python. You may be asked to solve real-world problems related to healthcare data architecture, showcasing your ability to design and implement effective data solutions.

3. Onsite Interviews

The onsite interview process typically consists of multiple rounds, often ranging from three to five interviews with various team members. These interviews will cover a mix of technical and behavioral questions. You can expect to discuss your experience with healthcare data standards, interoperability protocols, and your approach to collaborative projects. Additionally, you may be asked to present past projects or case studies that demonstrate your problem-solving skills and technical acumen.

4. Team Fit and Culture Interview

In one of the final rounds, you will likely participate in a team fit interview. This session focuses on assessing your interpersonal skills, communication style, and how well you align with Verily's values and mission. Expect to engage in discussions about teamwork, mentorship, and your approach to navigating challenges in a fast-paced environment.

5. Final Review

The last step in the interview process is a final review, where the interview panel will convene to discuss your performance across all rounds. They will evaluate your technical skills, cultural fit, and overall potential to contribute to Verily's goals. If successful, you will receive an offer that includes details about compensation, benefits, and next steps.

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

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

Understand the Healthcare Landscape

Given Verily's focus on transforming healthcare through data, it's crucial to familiarize yourself with the current trends and challenges in the healthcare industry. Be prepared to discuss how data architecture can address issues such as interoperability, data quality, and patient outcomes. Demonstrating a solid understanding of healthcare informatics will set you apart as a candidate who is not only technically proficient but also aligned with Verily's mission.

Showcase Your Technical Proficiency

As a Data Engineer, you will be expected to have a strong command of SQL, Python, and data modeling techniques. Prepare to discuss specific projects where you utilized these skills, particularly in the context of healthcare data. Highlight your experience with FHIR standards and interoperability protocols, as these are critical to the role. Consider bringing examples of your work, such as design documents or code snippets, to illustrate your expertise.

Emphasize Collaboration and Communication Skills

Verily values teamwork and collaboration across various functions. Be ready to share examples of how you have successfully worked with cross-functional teams, particularly in building data solutions. Highlight your interpersonal skills and your ability to mentor others, as these qualities are essential in a collaborative environment. Discuss how you approach consensus-building and problem-solving in team settings.

Prepare for Scenario-Based Questions

Expect to encounter scenario-based questions that assess your problem-solving abilities and technical knowledge. Think through potential challenges you might face in data integration or architecture design and how you would address them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you clearly articulate your thought process and the impact of your actions.

Align with Verily's Mission and Values

Verily is driven by a purpose to improve health outcomes through data. During your interview, express your passion for using data to make a difference in healthcare. Share any personal experiences or motivations that align with Verily's mission. This will demonstrate your commitment to the company's goals and culture, making you a more compelling candidate.

Stay Adaptable and Open-Minded

The fast-paced nature of Verily means that priorities can shift quickly. Be prepared to discuss how you handle change and adapt to new challenges. Share examples of how you have successfully navigated evolving timelines and project scopes in your previous roles. This adaptability will resonate well with the interviewers, showcasing your readiness to thrive in a dynamic environment.

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

Verily Data Engineer Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Verily data engineer interview. The role will require a strong understanding of data architecture, healthcare informatics, and data integration, as well as proficiency in relevant programming languages and tools. Be prepared to discuss your experience with healthcare data standards and your approach to building scalable data solutions.

Technical Skills

1. Can you explain the process of transforming healthcare data into FHIR-compliant representations?

Understanding FHIR compliance is crucial for this role, as it ensures interoperability in healthcare data exchange.

How to Answer

Discuss the steps involved in transforming data, including mapping existing data to FHIR resources, validating against FHIR profiles, and ensuring adherence to internal standards.

Example

“To transform healthcare data into FHIR-compliant representations, I start by analyzing the existing data structure and identifying the relevant FHIR resources. I then map the data fields to the corresponding FHIR attributes, ensuring that all required elements are included. After that, I validate the transformed data against FHIR profiles to ensure compliance and perform necessary adjustments to meet internal standards.”

2. Describe your experience with data modeling and normalization in healthcare.

This question assesses your practical experience in creating efficient data models that support healthcare applications.

How to Answer

Highlight specific projects where you designed data models, focusing on the techniques you used for normalization and the impact on data quality.

Example

“In my previous role, I designed a data model for a healthcare analytics platform that required normalization to reduce redundancy. I utilized third normal form (3NF) to structure the data, which improved data integrity and query performance. This model allowed for more accurate reporting and analysis of patient outcomes.”

3. What strategies do you use for data integration from disparate healthcare sources?

This question evaluates your approach to integrating various data sources, which is essential for a comprehensive healthcare data architecture.

How to Answer

Discuss the tools and protocols you use for data integration, as well as any challenges you’ve faced and how you overcame them.

Example

“I employ a combination of ETL processes and standard interoperability exchange protocols like HL7 and FHIR for data integration. In a recent project, I faced challenges with inconsistent data formats from EMRs and claims data. I implemented a robust data mapping strategy that standardized the incoming data, allowing for seamless integration and improved data quality.”

4. How do you ensure data quality and adherence to data standards in your projects?

Data quality is critical in healthcare; this question assesses your methods for maintaining high standards.

How to Answer

Explain your approach to data validation, monitoring, and the tools you use to ensure compliance with healthcare data standards.

Example

“I prioritize data quality by implementing automated validation checks during the data ingestion process. I use tools like dbt to create tests that ensure data adheres to defined standards, such as SNOMED and LOINC. Additionally, I regularly conduct data audits to identify and rectify any discrepancies, ensuring that our datasets remain reliable for analysis.”

Collaboration and Communication

5. Describe a time when you collaborated with cross-functional teams to build a data solution.

Collaboration is key in this role; this question assesses your teamwork and communication skills.

How to Answer

Share a specific example that highlights your role in the collaboration, the teams involved, and the outcome of the project.

Example

“I worked on a project where I collaborated with product managers and software engineers to develop a data pipeline for real-time analytics. I facilitated regular meetings to align our goals and ensure everyone was on the same page. This collaboration resulted in a successful launch of the pipeline, which improved our ability to make data-driven decisions quickly.”

6. How do you mentor junior team members in data engineering practices?

Mentorship is an important aspect of team dynamics; this question evaluates your leadership skills.

How to Answer

Discuss your approach to mentoring, including any specific techniques or resources you use to support junior team members.

Example

“I believe in hands-on mentorship, so I often pair program with junior engineers to guide them through complex tasks. I also encourage them to take ownership of smaller projects, providing support and feedback along the way. This approach not only helps them grow their skills but also fosters a collaborative team environment.”

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

Conflict resolution is essential for maintaining a productive work environment; this question assesses your interpersonal skills.

How to Answer

Describe a specific situation where you successfully resolved a conflict, focusing on your communication and problem-solving skills.

Example

“In a previous project, there was a disagreement between the data engineering and product teams regarding data requirements. I organized a meeting where both sides could express their concerns and needs. By facilitating open communication, we were able to reach a compromise that satisfied both teams, ultimately leading to a more effective data solution.”

8. What is your experience with cloud computing platforms in data engineering?

This question assesses your familiarity with cloud technologies, which are often used in modern data architectures.

How to Answer

Discuss the cloud platforms you’ve worked with, the types of projects you’ve used them for, and any specific tools or services you utilized.

Example

“I have extensive experience with AWS and Google Cloud Platform, where I’ve built data pipelines using services like AWS Lambda and Google BigQuery. In one project, I leveraged AWS S3 for data storage and used AWS Glue for ETL processes, which significantly improved our data processing speed and scalability.”