Remitly Data Engineer Interview Questions + Guide in 2025

Remitly is dedicated to transforming the way people send money across borders, empowering migrants to connect with their loved ones through innovative financial services.

As a Data Engineer at Remitly, you will be instrumental in developing and maintaining the ML platform's feature store, ensuring that machine learning models have timely access to high-quality data. Your responsibilities will include designing and implementing scalable data pipelines, performing ETL processes, and monitoring feature quality through data analysis and visualization. You will collaborate closely with data scientists, ML engineers, and other stakeholders to understand their data needs and troubleshoot any data-related issues that arise. Additionally, you will contribute to the data modeling and architecture of Remitly's data lakehouse and warehouse environments while providing technical support on data querying and access.

To excel in this role, candidates should possess a strong background in data engineering, particularly within machine learning contexts. Proficiency in data modeling, large-scale data processing systems like Apache Spark, and experience with AWS cloud services are essential. Strong communication skills and a collaborative mindset are crucial for effectively working with cross-functional teams at Remitly.

This guide aims to equip you with insights and strategies that will enhance your preparation for the interview process, helping you to confidently present your skills and experiences in alignment with Remitly's mission and values.

The interview process for a Data Engineer at Remitly is structured to assess both technical skills and cultural fit within the organization. It typically consists of several stages, each designed to evaluate different aspects of your qualifications and experiences.

1. Initial Screening

The process begins with a phone screening conducted by a recruiter. This initial call usually lasts around 30 minutes and focuses on your background, relevant experiences, and motivations for applying to Remitly. The recruiter will also provide an overview of the role and the company culture, ensuring that you have a clear understanding of what to expect.

2. Technical Assessment

Following the initial screening, candidates are often required to complete a technical assessment. This may involve a take-home project or a timed coding challenge that tests your proficiency in SQL, data modeling, and possibly Python. The assessment is designed to evaluate your ability to solve real-world data engineering problems and may take several hours to complete.

3. Technical Interview

Once you pass the technical assessment, you will typically have a technical interview with a member of the engineering team. This interview focuses on your technical skills, including your experience with data pipelines, ETL processes, and cloud technologies such as AWS. You may be asked to solve coding problems in real-time or discuss your approach to debugging and optimizing data pipelines.

4. Onsite Interview Loop

The final stage usually consists of an onsite interview loop, which can be conducted virtually. This loop typically includes multiple interviews with various team members, including data scientists, ML engineers, and other stakeholders. Each interview lasts about an hour and may cover a mix of technical questions, behavioral questions, and case studies. Interviewers will assess your problem-solving abilities, collaboration skills, and how well you align with Remitly's values.

5. Final Discussion

After the onsite interviews, there may be a final discussion with the hiring manager or a senior leader. This conversation often focuses on your fit within the team and the company culture, as well as any remaining questions you may have about the role or the organization.

As you prepare for your interviews, be ready to discuss your past projects, particularly those that demonstrate your experience with data engineering and machine learning platforms.

Next, let's delve into the specific interview questions that candidates have encountered during the process.

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

Understand the Role and Its Impact

As a Data Engineer at Remitly, your role is pivotal in supporting machine learning initiatives. Familiarize yourself with how your work will directly impact the feature store and the overall ML platform. Be prepared to discuss how you can ensure timely and high-quality feature availability, and think about specific examples from your past experiences that demonstrate your ability to design and maintain scalable data pipelines.

Prepare for Technical Assessments

Given the emphasis on SQL and algorithms in the interview process, ensure you are well-versed in these areas. Brush up on SQL queries, particularly those involving complex joins and data transformations. Additionally, practice algorithmic problems that require you to think critically about data structures and optimization. Expect to demonstrate your proficiency in debugging and optimizing data pipelines, especially in a Spark environment.

Showcase Your Collaboration Skills

Remitly values cross-functional collaboration, so be ready to discuss how you have worked with data scientists, ML engineers, and other stakeholders in previous roles. Prepare examples that highlight your ability to understand and meet the data requirements of various teams. Emphasize your communication skills and how you can bridge the gap between technical and non-technical stakeholders.

Emphasize Problem-Solving Abilities

The interview process will likely include situational questions that assess your problem-solving skills. Be prepared to discuss specific challenges you faced in previous projects, how you approached them, and the outcomes. Highlight your analytical skills and your ability to troubleshoot and resolve data-related issues effectively.

Familiarize Yourself with Company Culture

Remitly places a strong emphasis on its values and culture. Research the company’s mission and values, and think about how your personal values align with them. Be prepared to discuss how you embody these values in your work. This alignment will be crucial in demonstrating that you are a good fit for the team.

Prepare for Behavioral Questions

Expect a mix of technical and behavioral questions during your interviews. Use the STAR (Situation, Task, Action, Result) method to structure your responses to behavioral questions. This will help you articulate your experiences clearly and effectively. Focus on scenarios that showcase your adaptability, teamwork, and leadership skills.

Be Ready for a Comprehensive Interview Process

The interview process at Remitly can be lengthy and involve multiple rounds. Stay organized and be prepared for various formats, including coding challenges, case studies, and panel interviews. Approach each stage with a positive attitude and view it as an opportunity to learn more about the company and its people.

Follow Up Thoughtfully

After your interviews, consider sending a follow-up email to express your gratitude for the opportunity and reiterate your interest in the role. This is also a chance to reflect on any specific points discussed during the interview that you found particularly engaging or relevant.

By preparing thoroughly and aligning your experiences with the expectations of the role, you can position yourself as a strong candidate for the Data Engineer position at Remitly. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Remitly. The interview process will focus on your technical skills, particularly in data engineering, machine learning platforms, and your ability to work collaboratively with cross-functional teams. Be prepared to discuss your past experiences in detail, as well as demonstrate your problem-solving abilities through practical scenarios.

Technical Skills

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

Understanding the ETL process is crucial for a Data Engineer, as it forms the backbone of data management.

How to Answer

Discuss your experience with designing and implementing ETL pipelines, including the tools and technologies you used. Highlight any challenges you faced and how you overcame them.

Example

“In my previous role, I designed an ETL pipeline using Apache Spark to process large datasets from various sources. I implemented data validation checks to ensure data quality and used AWS S3 for storage. One challenge was optimizing the pipeline for speed, which I addressed by partitioning the data and using efficient data formats like Parquet.”

2. Describe your experience with data modeling and how it impacts data quality.

Data modeling is essential for ensuring that data is structured correctly for analysis.

How to Answer

Explain your approach to data modeling, including any specific methodologies you follow. Discuss how good data modeling practices can enhance data quality.

Example

“I have extensive experience in data modeling using both star and snowflake schemas. I ensure that the models are normalized to reduce redundancy while maintaining performance. This approach has significantly improved data quality in my projects, as it allows for easier data validation and consistency checks.”

3. How do you monitor and troubleshoot data pipelines?

Monitoring and troubleshooting are key responsibilities for a Data Engineer.

How to Answer

Discuss the tools and techniques you use for monitoring data pipelines and how you approach troubleshooting when issues arise.

Example

“I use tools like Apache Airflow for monitoring my data pipelines. I set up alerts for failures and performance issues. When troubleshooting, I start by checking the logs to identify the root cause, and I often use data profiling techniques to ensure the data integrity before it enters the pipeline.”

4. Can you explain your experience with AWS services relevant to data engineering?

AWS is a critical component of many data engineering roles, especially at Remitly.

How to Answer

Detail your experience with specific AWS services, particularly those mentioned in the job description, and how you have utilized them in your projects.

Example

“I have worked extensively with AWS services such as EMR for big data processing, S3 for data storage, and Redshift for data warehousing. In one project, I used EMR to process terabytes of data, which improved our data processing time by 30% compared to our previous setup.”

Machine Learning Integration

5. How do you ensure feature quality for machine learning models?

Feature quality is vital for the success of machine learning models.

How to Answer

Discuss the methods you use to validate and monitor feature quality, including any specific metrics or tools.

Example

“I implement automated tests to validate features before they are used in models. I also monitor feature distributions over time to detect any drift. For instance, I used statistical tests to compare the distributions of features in training and production environments, ensuring that our models remain robust.”

6. Describe a time when you had to collaborate with data scientists to meet their data needs.

Collaboration is key in a cross-functional environment.

How to Answer

Provide a specific example of a project where you worked closely with data scientists, detailing the challenges and outcomes.

Example

“In a recent project, the data science team needed access to real-time data for their models. I collaborated with them to design a streaming data pipeline using Apache Kafka, which allowed us to deliver data in near real-time. This collaboration not only met their needs but also improved our overall data processing capabilities.”

Problem-Solving and Critical Thinking

7. Can you give an example of a complex data issue you resolved?

Problem-solving skills are essential for a Data Engineer.

How to Answer

Describe a specific data issue you encountered, the steps you took to resolve it, and the impact of your solution.

Example

“I once faced a situation where our data ingestion process was failing due to schema mismatches. I conducted a thorough analysis of the incoming data and identified discrepancies in the data types. I implemented a schema validation step in our pipeline, which not only resolved the issue but also prevented similar problems in the future.”

8. How do you approach optimizing data pipelines for performance?

Optimization is a critical aspect of data engineering.

How to Answer

Discuss the strategies you employ to optimize data pipelines, including any tools or techniques you use.

Example

“I focus on optimizing data pipelines by minimizing data movement and leveraging distributed computing. For example, I used partitioning and bucketing in Spark to reduce the amount of data shuffled during processing, which improved the performance of our ETL jobs significantly.”

Behavioral and Cultural Fit

9. How do you align your work with the company’s values?

Understanding and aligning with company values is important at Remitly.

How to Answer

Reflect on the company’s values and provide examples of how you embody them in your work.

Example

“I believe in customer-centricity, which aligns with Remitly’s values. In my previous role, I always sought feedback from end-users to understand their pain points better. This approach helped me design data solutions that were not only technically sound but also met the actual needs of the users.”

10. What motivates you to work in data engineering?

Understanding your motivation can help assess cultural fit.

How to Answer

Share your passion for data engineering and what drives you in this field.

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 excites me. I find it rewarding to see how my work can directly impact business outcomes and improve customer experiences.”