One Medical Data Engineer Interview Questions + Guide in 2025

One Medical is a modern healthcare company that aims to make it easier for patients to access and receive high-quality medical care through technology and innovation.

As a Data Engineer at One Medical, you will be responsible for designing, building, and maintaining scalable data pipelines that support the organization’s mission of improving healthcare delivery. This role requires a strong understanding of data architecture, SQL, and big data technologies such as Hadoop and Spark. You will be tasked with integrating data from various sources, ensuring data quality, and optimizing data storage and retrieval processes. Successful candidates will possess excellent problem-solving skills, a solid foundation in algorithms, and the ability to work collaboratively with data scientists, analysts, and other stakeholders to translate complex data into actionable insights. Your contributions will directly impact the efficiency of healthcare operations and enhance patient experiences.

This guide will help you prepare for a job interview by providing insights into the role's expectations, key skills to focus on, and the types of questions you may encounter during the interview process.

The interview process for a Data Engineer at One Medical is structured and designed to assess both technical and interpersonal skills. It typically consists of four main stages:

1. Recruiter Screen

The process begins with a 30-minute phone interview with a recruiter. This initial screen is focused on understanding your background, skills, and motivations for applying to One Medical. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role, ensuring that you have a clear understanding of what to expect moving forward.

2. Hiring Manager Interview

Following the recruiter screen, candidates will have a one-on-one interview with the hiring manager. This discussion delves deeper into your technical expertise, particularly in areas relevant to the role such as Hadoop, Spark, and data processing frameworks. The hiring manager will assess your problem-solving abilities and how your experience aligns with the team's needs.

3. Technical Phone Screen/Coding Challenge

The next step is a technical phone screen, which may include a coding challenge. This interview is typically conducted via a coding platform where you will be asked to solve problems in real-time. Expect questions that test your knowledge of SQL, data structures, and algorithms. You may also encounter system design questions that evaluate your ability to architect data solutions effectively.

4. Onsite Interview

The final stage is an onsite interview, which combines both behavioral and technical components. During this phase, you will participate in multiple rounds of interviews with various team members. These sessions will cover a range of topics, including your past projects, technical skills, and how you approach collaboration and problem-solving in a team environment. Be prepared to discuss your experiences in detail and demonstrate your technical proficiency through practical exercises.

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

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at One Medical. The interview process will assess your technical skills in data processing, system design, and your ability to work collaboratively within a team. Be prepared to demonstrate your proficiency in SQL, Python, and data engineering concepts, as well as your problem-solving abilities.

Technical Skills

1. Can you explain the differences between Hadoop and Spark?

Understanding the strengths and weaknesses of these frameworks is crucial for a Data Engineer role.

How to Answer

Discuss the core functionalities of both frameworks, highlighting their use cases, performance differences, and when to choose one over the other.

Example

“Hadoop is primarily used for batch processing and is great for handling large datasets, while Spark excels in real-time data processing and in-memory computations. I would choose Spark for applications requiring low latency and quick data processing, whereas Hadoop would be more suitable for large-scale data storage and batch jobs.”

2. Describe a data pipeline you have built. What technologies did you use?

This question assesses your practical experience in building data pipelines.

How to Answer

Detail the architecture of the pipeline, the technologies used, and the challenges faced during implementation.

Example

“I built a data pipeline using Apache Kafka for real-time data ingestion, Apache Spark for processing, and AWS S3 for storage. One challenge was ensuring data consistency during streaming, which I addressed by implementing idempotent writes.”

3. How do you optimize SQL queries for performance?

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

How to Answer

Discuss techniques such as indexing, query restructuring, and analyzing execution plans.

Example

“I optimize SQL queries by using indexing on frequently queried columns, avoiding SELECT *, and analyzing the execution plan to identify bottlenecks. For instance, I once reduced query execution time by 50% by rewriting a complex join into a series of simpler queries.”

4. What is your experience with ETL processes?

This question evaluates your understanding of Extract, Transform, Load processes.

How to Answer

Explain your role in ETL processes, the tools you used, and any challenges you overcame.

Example

“I have extensive experience with ETL processes using Apache NiFi. I designed a workflow that extracted data from various sources, transformed it to meet business requirements, and loaded it into a data warehouse. A key challenge was ensuring data quality, which I addressed by implementing validation checks at each stage.”

5. Can you explain the CAP theorem and its implications for distributed systems?

Understanding the CAP theorem is essential for designing robust data systems.

How to Answer

Define the CAP theorem and discuss its implications on consistency, availability, and partition tolerance.

Example

“The CAP theorem states that in a distributed system, you can only guarantee two of the three properties: consistency, availability, and partition tolerance. This means that during a network partition, I must choose between maintaining consistency or availability, which impacts how I design data systems based on the specific use case.”

Behavioral Questions

1. Tell me about a recent project you worked on. What was your role?

This question helps assess your teamwork and project management skills.

How to Answer

Describe the project, your specific contributions, and the outcome.

Example

“I recently worked on a project to migrate our data warehouse to a cloud-based solution. As the lead data engineer, I coordinated with cross-functional teams to ensure a smooth transition, which resulted in a 30% reduction in query times and improved scalability.”

2. How do you handle tight deadlines and pressure?

This question evaluates your ability to work under pressure.

How to Answer

Share a specific example of a time you successfully managed a tight deadline.

Example

“During a critical project, we faced a tight deadline due to unexpected data quality issues. I prioritized tasks, communicated transparently with stakeholders, and worked extra hours to ensure we met the deadline without compromising quality.”

3. Describe a time you had a conflict with a team member. How did you resolve it?

This question assesses your interpersonal skills and conflict resolution abilities.

How to Answer

Discuss the situation, your approach to resolving the conflict, and the outcome.

Example

“I had a disagreement with a colleague over the choice of technology for a project. I suggested we hold a meeting to discuss our perspectives and evaluate the pros and cons of each option. This open dialogue led us to a consensus on the best approach, strengthening our collaboration.”

4. How do you stay updated with the latest technologies in data engineering?

This question gauges your commitment to continuous learning.

How to Answer

Share the resources you use to stay informed about industry trends and technologies.

Example

“I regularly read industry blogs, participate in online forums, and attend webinars. I also take online courses to deepen my understanding of emerging technologies, such as cloud computing and machine learning, which are increasingly relevant in data engineering.”

5. What motivates you to work in data engineering?

This question helps interviewers understand your passion for the field.

How to Answer

Discuss what aspects of data engineering excite you and how they align with your career goals.

Example

“I am motivated by the challenge of transforming raw data into actionable insights. The ability to solve complex problems and contribute to data-driven decision-making in healthcare is particularly fulfilling for me, as I believe it can significantly improve patient outcomes.”