Fetch Rewards, Inc. Data Engineer Interview Questions + Guide in 2025

Fetch Rewards, Inc. is a leading mobile application that allows users to earn rewards for scanning their grocery receipts, providing an innovative way to engage customers and drive brand loyalty.

As a Data Engineer at Fetch Rewards, you will be responsible for designing, constructing, and maintaining scalable data pipelines that support the analytics and data science needs of the organization. This role entails working with various data sources, implementing ETL processes, and ensuring data quality and accessibility across teams. You will collaborate closely with data scientists, analysts, and product teams to provide insights that drive strategic decision-making. Required skills include proficiency in SQL and Python, experience with cloud services (preferably AWS), and familiarity with relational databases such as PostgreSQL. A strong analytical mindset, attention to detail, and effective communication skills are essential to excel in this position.

Given Fetch's emphasis on customer engagement and innovative solutions, a successful Data Engineer will not only possess technical expertise but also share the company's commitment to enhancing user experience through data-driven insights. This guide is designed to help you understand the expectations for the role and prepare effectively for your interview, ensuring you can showcase your skills and alignment with Fetch Rewards' mission.

The interview process for a Data Engineer role at Fetch Rewards is structured to assess both technical skills and cultural fit. It typically consists of several stages, each designed to evaluate different competencies relevant to the position.

1. Application and Initial Assessment

After submitting your application, candidates are often required to complete a take-home assessment. This assessment usually involves tasks related to data manipulation, SQL queries, and possibly some coding challenges. While the time to complete this assessment is not strictly limited, candidates are generally encouraged to finish it within 48 hours. This initial step is crucial as it sets the tone for the rest of the interview process.

2. Technical Screening

Following the successful completion of the take-home assessment, candidates typically move on to a technical screening. This is often conducted via a video call and focuses on evaluating the candidate's technical knowledge and problem-solving abilities. Interviewers may ask candidates to solve SQL problems in real-time, discuss their previous projects, and demonstrate their understanding of data engineering principles. This round may also include questions about Python and other relevant technologies.

3. Case Study Interview

Candidates who perform well in the technical screening may be invited to a case study interview. This round often involves analyzing a specific dataset or business problem related to Fetch Rewards. Candidates are expected to demonstrate their analytical skills and ability to derive insights from data. This interview may also include discussions about how to structure data pipelines and manage data flow, reflecting the practical aspects of the role.

4. Onsite Interview

The final stage of the interview process is typically an onsite interview, which may be conducted virtually. This round usually consists of multiple interviews with different team members, including data engineers, analysts, and possibly management. Candidates can expect a mix of technical questions, behavioral questions, and discussions about their fit within the team and company culture. The onsite interview may also include practical coding challenges and problem-solving exercises that require candidates to think on their feet.

Throughout the interview process, candidates are encouraged to ask clarifying questions and engage with the interviewers to demonstrate their thought process. However, it's important to balance this with the need to provide clear and concise answers to the interviewers' questions.

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

Here are some tips to help you excel in your interview for the Data Engineer role at Fetch Rewards, Inc.

Familiarize Yourself with the Fetch Rewards App

Before your interview, download and explore the Fetch Rewards app. Understanding its functionality and user experience will not only help you answer questions about the product but also demonstrate your genuine interest in the company. Be prepared to discuss your thoughts on the app and any potential improvements you might suggest. This shows that you are proactive and engaged, which aligns with the company’s culture.

Prepare for Technical Assessments

Expect a significant focus on technical skills, particularly in SQL and Python. Review key concepts and practice coding challenges that involve data manipulation, ETL processes, and database design. Given the emphasis on practical assessments, consider working on projects that mimic the types of tasks you might encounter in the role, such as building APIs or processing JSON data. Familiarize yourself with common data structures and algorithms, as well as the specific technologies mentioned in the job description.

Ask Clarifying Questions

During the interview, don’t hesitate to ask clarifying questions, especially when presented with coding challenges or case studies. This not only helps you understand the expectations better but also demonstrates your analytical thinking and communication skills. However, be mindful of the balance; while asking questions is encouraged, ensure that you are also providing clear and concise answers to their queries.

Showcase Your Problem-Solving Skills

When discussing your past experiences or during technical assessments, focus on your problem-solving approach. Explain your thought process clearly, and be prepared to discuss how you would tackle specific challenges related to data engineering. Highlight any relevant projects where you successfully implemented solutions, particularly those that involved data processing or database management.

Be Ready for Behavioral Questions

While technical skills are crucial, Fetch Rewards also values cultural fit. Prepare for behavioral questions that assess your teamwork, adaptability, and how you handle challenges. Reflect on past experiences where you demonstrated these qualities, and be ready to share specific examples. This will help you convey that you are not only technically proficient but also a good fit for the team.

Manage Your Time Wisely

The interview process can be lengthy and may involve multiple rounds, including take-home assessments and technical interviews. Be strategic about your time management, especially when completing take-home assignments. While it’s important to deliver quality work, ensure that you don’t spend excessive time on a single task, as this can lead to burnout and frustration.

Stay Positive and Professional

Throughout the interview process, maintain a positive and professional demeanor, even if you encounter challenges or frustrations. The feedback from candidates indicates that the interviewers are generally friendly and passionate about their work. Engaging with them positively can leave a lasting impression and may even help you stand out among other candidates.

By following these tips and preparing thoroughly, you can enhance your chances of success in the interview process at Fetch Rewards, Inc. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Fetch Rewards, Inc. The interview process will likely focus on your technical skills, particularly in SQL, Python, and data processing, as well as your ability to communicate effectively and solve real-world problems. Be prepared to demonstrate your knowledge of data engineering principles, database management, and your experience with data pipelines.

Technical Skills

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 a fundamental part of data management.

How to Answer

Discuss your experience with ETL processes, including the tools you used and the challenges you faced. Highlight specific projects where you successfully implemented ETL.

Example

“In my previous role, I designed an ETL pipeline using Apache Airflow to automate data extraction from various sources, transform it using Python scripts, and load it into a PostgreSQL database. This process improved data availability for analytics by 30%.”

2. Describe a challenging SQL query you wrote and the problem it solved.

SQL proficiency is essential for data manipulation and retrieval.

How to Answer

Provide a specific example of a complex SQL query you wrote, explaining the context and the outcome. Focus on the logic behind your query and any optimizations you made.

Example

“I once had to write a SQL query to analyze customer purchase patterns. The query involved multiple joins and subqueries to aggregate data from different tables. By optimizing the query with indexing, I reduced the execution time from several minutes to under 30 seconds.”

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

Data quality is critical in data engineering, and interviewers will want to know your approach to maintaining it.

How to Answer

Discuss the methods you use to validate and clean data, as well as any tools or frameworks you employ to monitor data quality.

Example

“I implement data validation checks at each stage of the ETL process, using tools like Great Expectations to ensure data quality. Additionally, I set up alerts for any anomalies detected in the data, allowing for quick remediation.”

4. What experience do you have with cloud platforms and data storage solutions?

Familiarity with cloud services is increasingly important in data engineering roles.

How to Answer

Mention specific cloud platforms you have worked with, such as AWS, Google Cloud, or Azure, and describe how you utilized their data storage solutions.

Example

“I have extensive experience with AWS, particularly with S3 for data storage and Redshift for data warehousing. I designed a data lake architecture that allowed for scalable storage and efficient querying of large datasets.”

5. Can you explain the concept of data normalization and why it is important?

Data normalization is a key concept in database design that helps reduce redundancy.

How to Answer

Define data normalization and discuss its benefits, including how it impacts database performance and data integrity.

Example

“Data normalization is the process of organizing data to minimize redundancy. It’s important because it ensures data integrity and improves query performance by reducing the amount of data that needs to be processed.”

Problem-Solving and Case Studies

6. Describe a time when you had to troubleshoot a data pipeline issue. What steps did you take?

Problem-solving skills are essential for a Data Engineer, especially when dealing with data pipeline failures.

How to Answer

Outline the specific issue you encountered, the steps you took to diagnose and resolve it, and the outcome of your actions.

Example

“When a data pipeline failed due to a schema change in the source database, I quickly identified the issue by reviewing logs and tracing the data flow. I updated the transformation scripts to accommodate the new schema, which restored the pipeline functionality within an hour.”

7. How would you approach designing a data model for a new application?

Data modeling is a critical skill for Data Engineers, and interviewers will want to see your thought process.

How to Answer

Discuss the steps you would take to gather requirements, design the model, and ensure it meets the application’s needs.

Example

“I would start by gathering requirements from stakeholders to understand the data needs. Then, I would create an entity-relationship diagram to visualize the data model, ensuring normalization and scalability. Finally, I would validate the model with the team before implementation.”

8. What strategies do you use for optimizing database performance?

Performance optimization is a key responsibility for Data Engineers.

How to Answer

Share specific techniques you have used to improve database performance, such as indexing, query optimization, or partitioning.

Example

“I regularly analyze query performance and use indexing to speed up data retrieval. In one project, I implemented partitioning on a large table, which reduced query times by over 50%.”

9. How do you handle version control for your data pipelines and scripts?

Version control is important for collaboration and maintaining code quality.

How to Answer

Discuss the tools you use for version control and how you manage changes to your data pipelines.

Example

“I use Git for version control, allowing me to track changes to my data pipeline scripts. I also implement branching strategies to manage feature development and ensure that the main branch remains stable.”

10. Can you give an example of how you have used Python in your data engineering work?

Python is a common language used in data engineering for scripting and automation.

How to Answer

Provide a specific example of a project where you used Python, detailing the libraries and frameworks you utilized.

Example

“I used Python with Pandas to clean and transform a large dataset for analysis. I wrote scripts to automate the data cleaning process, which saved the team several hours of manual work each week.”