GoodRx Data Engineer Interview Questions + Guide in 2025

GoodRx is America's healthcare marketplace, providing millions with access to affordable prescription medications and telehealth services each month. The company has saved consumers over $60 billion since its inception and continues to innovate in the healthcare sector.

As a Data Engineer at GoodRx, you will play a vital role in building and maintaining robust data pipelines and architectures that support the company's mission of making healthcare affordable and accessible. Your key responsibilities will include collaborating with product managers, data scientists, and other engineers to define data specifications, design and deploy data warehouse solutions, and develop processing pipelines utilizing cloud technologies like AWS. You will ensure the integrity and usability of data, lead projects to solve complex data challenges, and enhance overall data infrastructure.

To excel in this role, you should possess strong technical skills in SQL, Python, and ETL processes, with a focus on big data technologies. Your ability to analyze large datasets to identify gaps and propose solutions is essential. GoodRx values curiosity, collaboration, and efficiency, so an innate curiosity to learn new technologies and a proactive approach to problem-solving will make you a great fit.

This guide is designed to help you prepare effectively for your interview by providing insights into the role's expectations and the skills that are most relevant to GoodRx's data engineering needs.

The interview process for a Data Engineer at GoodRx is designed to assess both technical skills and cultural fit within the company. It typically consists of multiple stages, each focusing on different aspects of the candidate's abilities and experiences.

1. Initial Phone Screen

The process begins with an initial phone screen, usually lasting about 30-45 minutes. During this call, a recruiter will discuss the role, the company culture, and your background. This is an opportunity for you to express your interest in the position and to highlight your relevant experiences, particularly in data engineering and cloud technologies.

2. Technical Screen

Following the initial screen, candidates typically undergo a technical screen, which may be conducted via video call. This session focuses on your coding skills, particularly in SQL and Python, as well as your understanding of data architecture and ETL processes. You may be asked to solve coding problems in real-time, demonstrating your ability to think critically and debug issues as they arise.

3. Additional Phone Screen

After the technical screen, there may be another phone interview, which often dives deeper into your past experiences and projects. This conversation may include behavioral questions that assess how you collaborate with cross-functional teams, such as product managers and data scientists, to define requirements and specifications for data solutions.

4. Live Coding Assessment

Candidates will then participate in a live coding assessment, where you will be asked to solve specific data engineering problems. This may involve writing complex SQL queries or developing data processing pipelines using tools like AWS, Databricks, or Airflow. The focus here is on your ability to apply your technical knowledge to real-world scenarios.

5. Onsite Interview (Power Day)

The final stage is an onsite interview, often referred to as a "power day." This consists of multiple interview sessions, typically five, where you will meet with various team members, including engineers and managers. Each session will cover different topics, such as data pipeline architecture, data integrity, and collaboration strategies. Expect a mix of technical and behavioral questions, as well as discussions about your approach to problem-solving and project management.

Throughout the interview process, GoodRx emphasizes a collaborative and supportive environment, so be prepared to engage in discussions that reflect this culture.

Next, let's explore the types of questions you might encounter during these interviews.

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

Embrace the Collaborative Culture

GoodRx values collaboration and teamwork, as evidenced by the positive feedback from candidates about the interactive nature of the interviews. Approach your interview with a mindset of collaboration. Be prepared to discuss how you have worked with cross-functional teams in the past, particularly with product managers, data scientists, and other engineers. Highlight your ability to define requirements and data specifications collaboratively, as this will resonate well with the company’s culture.

Prepare for Thought-Provoking Questions

Candidates have noted that the interview questions at GoodRx are designed to be thought-provoking and relevant to the role. Expect questions that require you to think critically about data engineering challenges. Prepare to discuss your preferences and experiences with various frameworks and technologies, and be ready to articulate how you would approach specific data problems. This will demonstrate your analytical skills and your ability to apply your knowledge in practical scenarios.

Showcase Your Technical Proficiency

Given the emphasis on SQL and Python in the role, ensure you are well-versed in these technologies. Brush up on your SQL skills, particularly complex queries, and be prepared to demonstrate your proficiency in Python, especially in the context of data processing and ETL development. Familiarize yourself with big data technologies and cloud services like AWS, Databricks, and Airflow, as these are crucial for the role. Be ready to discuss your experience with data pipelines and how you have utilized these tools in past projects.

Demonstrate Problem-Solving Skills

The interview process includes live coding sessions where you may be asked to debug data pipeline issues or solve data-related problems. Practice coding challenges that focus on data manipulation and pipeline construction. Be prepared to walk through your thought process when debugging or optimizing a data pipeline, as this will showcase your problem-solving abilities and technical expertise.

Be Authentic and Personable

GoodRx has a reputation for a friendly and supportive interview environment. Don’t hesitate to show your personality and be authentic during the interview. Share your passion for data engineering and how it aligns with GoodRx’s mission to provide affordable healthcare solutions. This will help you connect with your interviewers on a personal level and demonstrate that you are a good cultural fit for the company.

Follow Up with Gratitude

After your interview, take the time to send a thoughtful thank-you note to your interviewers. Express your appreciation for the opportunity to interview and reiterate your enthusiasm for the role and the company. This small gesture can leave a lasting impression and reinforce your interest in joining the GoodRx team.

By following these tips, you will be well-prepared to navigate the interview process at GoodRx and demonstrate that you are the right fit for the Data Engineer role. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at GoodRx. The interview process will focus on your technical skills, problem-solving abilities, and your experience with data engineering concepts, particularly in building and maintaining data pipelines, working with cloud technologies, and collaborating with cross-functional teams.

Data Engineering Concepts

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

Understanding the ETL (Extract, Transform, Load) process is crucial for a Data Engineer, as it is fundamental to data pipeline development.

How to Answer

Discuss your experience with each stage of the ETL process, emphasizing specific tools and technologies you have used. Highlight any challenges you faced and how you overcame them.

Example

“In my previous role, I implemented an ETL process using Apache Airflow for scheduling and managing workflows. I extracted data from various sources, transformed it using Python scripts to clean and aggregate the data, and then loaded it into a Redshift data warehouse. One challenge was ensuring data quality, which I addressed by implementing validation checks at each stage of the process.”

2. How do you ensure data quality and integrity in your data pipelines?

Data quality is essential for reliable analytics and decision-making.

How to Answer

Explain the methods and tools you use to monitor and validate data quality. Discuss any specific metrics or processes you have implemented.

Example

“I ensure data quality by implementing automated validation checks during the ETL process. For instance, I use dbt to create tests that check for null values and data type mismatches. Additionally, I set up alerts to notify the team of any discrepancies, allowing us to address issues proactively.”

3. Describe a challenging data pipeline you built and how you approached the problem.

This question assesses your problem-solving skills and technical expertise.

How to Answer

Provide a specific example of a complex data pipeline you developed, detailing the challenges you faced and the solutions you implemented.

Example

“I once built a data pipeline that integrated real-time data from multiple sources using AWS Kinesis. The challenge was ensuring low latency while processing large volumes of data. I optimized the pipeline by using partitioning strategies and leveraging AWS Lambda for serverless processing, which significantly improved performance.”

SQL and Data Modeling

4. What are some advanced SQL techniques you have used in your projects?

SQL proficiency is critical for data manipulation and analysis.

How to Answer

Discuss specific SQL techniques you have employed, such as window functions, CTEs, or complex joins, and how they benefited your projects.

Example

“I frequently use window functions to perform calculations across a set of rows related to the current row. For example, I used a window function to calculate running totals for sales data, which helped the business identify trends over time. Additionally, I utilize CTEs to simplify complex queries and improve readability.”

5. How do you approach data modeling for a new project?

Data modeling is essential for structuring data effectively.

How to Answer

Outline your process for data modeling, including how you gather requirements and design the schema.

Example

“When starting a new project, I first collaborate with stakeholders to understand their data needs. I then create an ERD (Entity-Relationship Diagram) to visualize the relationships between entities. I focus on normalization to reduce redundancy while ensuring that the model supports the necessary queries and reporting requirements.”

Cloud Technologies

6. Can you describe your experience with AWS services relevant to data engineering?

Familiarity with cloud services is crucial for modern data engineering roles.

How to Answer

Detail your experience with specific AWS services, such as S3, Redshift, or Glue, and how you have used them in your projects.

Example

“I have extensive experience with AWS S3 for data storage and Redshift for data warehousing. I often use S3 to store raw data and then leverage AWS Glue to catalog and prepare the data for analysis in Redshift. This setup allows for efficient querying and reporting, which has been beneficial for our analytics team.”

7. How do you handle data security and compliance in your data pipelines?

Data security is a critical concern for any organization handling sensitive information.

How to Answer

Discuss the security measures you implement in your data pipelines, including encryption, access controls, and compliance with regulations.

Example

“I prioritize data security by implementing encryption both at rest and in transit. I also use IAM roles to control access to AWS resources, ensuring that only authorized personnel can access sensitive data. Additionally, I stay informed about compliance requirements, such as HIPAA, and ensure that our data handling practices align with these regulations.”

Collaboration and Problem-Solving

8. Describe a time when you had to collaborate with cross-functional teams. How did you ensure effective communication?

Collaboration is key in a data engineering role, as you will work with various stakeholders.

How to Answer

Provide an example of a project where you collaborated with different teams, emphasizing your communication strategies.

Example

“In a recent project, I collaborated with data scientists and product managers to define data requirements for a new feature. I organized regular meetings to discuss progress and gather feedback, and I used tools like Jira to track tasks and ensure transparency. This approach fostered a collaborative environment and helped us meet our deadlines.”

9. How do you approach debugging issues in your data pipelines?

Debugging is an essential skill for maintaining data integrity.

How to Answer

Explain your systematic approach to identifying and resolving issues in data pipelines.

Example

“When debugging data pipelines, I start by reviewing logs to identify where the failure occurred. I then isolate the problematic component, whether it’s an ETL job or a data source, and run tests to pinpoint the issue. For example, I once encountered a data mismatch due to a schema change in the source system, which I resolved by updating the transformation logic accordingly.”