PayPal Data Engineer Interview Questions + Guide in 2025

PayPal is actively implementing a Data Mesh architecture, moving away from traditional centralized data platforms toward a more distributed, domain-oriented model in 2025. This transition began in 2021 and has since become a major focus for the data engineering team. Data engineers at PayPal manage more than 1.1 petabytes of data each day. They handle around 13 million transactions and collect over 20 terabytes of log data daily. As a result of this scale and complexity, the PayPal data engineer interview is considered one of the most challenging in the industry, only accepting talented individuals focused on innovation and action.

Role Overview & Culture

Given the scale of the data the company handles, PayPal data engineers engage in a diverse range of activities that center around building and maintaining the infrastructure supporting the company’s massive processing needs. You, as a data engineer at PayPal, will spend significant time developing, testing, and deploying data integration and data ingestion pipelines, in addition to optimizing ETL processes to ensure high-quality data across various platforms. Performing data analysis and testing for corner scenarios is also involved in a PayPal data engineer’s routine.

While cross-collaboration is not a huge part of the deal, you’ll often find yourself communicating with stakeholders to understand the business objectives and user needs. In the tech stack, Java is used for the backend, while Python is mostly employed for the analysis and ML tasks. Some of the teams also use JavaScript for both front and back-end, particularly lenient to Node.js. As you might also expect, big data is critical as well at PayPal, requiring data engineers to be proficient in Apache Spark and Hadoop.

Why This Role at PayPal?

If you’re looking to make a significant impact in the fintech sector while working with spearheading technology like agentic-AI, PayPal is the place for you to work as a data engineer. The company provides a highly competitive base salary along with a lucrative Employee Stock Purchase Plan. Benefits also include both short and long-term disability coverage, as well as sabbaticals.

Data engineers at PayPal also proudly highlight their involvement in the real-time fraud detection systems, analyzing thousands of data points and calculating up to 300 variables per event. The role also supports work-life balance through a hybrid model, with three days in the office and two days of your choosing. PayPal offers well-defined career progression paths for data engineers. You can grow from Data Engineer to Senior, then to Staff or Principal Data Engineer. There are also opportunities to move into leadership and management roles.

The PayPal data engineer interview process includes multiple stages that assess your technical skills, system design knowledge, and cultural alignment, with variations based on your experience level and the role’s requirements. Here’s an overview of the Interview Process:

• Application Submission
• Recruiter Screen
• Technical Rounds
• Onsite/Virtual Loop
• Hiring Committee

Application Submission

The PayPal data engineer interview process kicks off with you creating an account on the career portal and submitting your application. While having a referral improves visibility, it doesn’t guarantee moving up the interview process. Your resume will automatically pass through an advanced matching feature that evaluates your skills and qualifications against the requirements of specific positions. If you’ve submitted more than one application, PayPal recommends informing the recruitment team when contacted. The initial review may take a week or two, during which recruiters screen applications and shortlist candidates.

Recruiter Screen

If your application is shortlisted and moved up, a recruiter from the PayPal Talent Acquisition team will make contact via a 30-minute phone call or invite you for a video meeting. The recruiter is likely to discuss the position details and your relevant experience before allowing you to tell your story about your career trajectory. This round is designed to be a two-way discussion. The recruiter will seek information beyond what’s on your resume, and it’s your responsibility to be prepared with summaries of your past accomplishments, present situation, and motivation.

Technical Rounds

The technical rounds of PayPal data engineer interviews are divided into 1-2 parts, each 45 to 60 minutes, with the first part being an online assessment (OA), which is being phased out rapidly. If you complete the fundamental coding challenge presented in HackerRank or similar platforms, you’ll be asked to take the next technical screening assessments.

The next segment of the deeper technical rounds focuses on SQL and DSA questions and lasts about an hour. You’ll be asked to live code with a senior engineer present, expecting you to go through your problem-solving approach and algorithmic thinking. If your interviewer is satisfied with your solution, you’ll be invited to the onsite loop.

Onsite/Virtual Loop

The PayPal Data Engineer onsite interview loop typically consists of 3-4 rounds conducted in a single day or spread across multiple days, with each interview lasting between 45-60 minutes. These rounds are designed to thoroughly assess both your technical capabilities and cultural fit with PayPal’s values of Inclusion, Innovation, Collaboration, and Wellness.

The first technical round of the onsite loop is typically conducted with a Data Engineer III and revolves around more in-depth concepts of SQL and DSA. This round is particularly important as PayPal processes billions of SQL queries daily through their HERA system. Expect problems focusing on window functions, priority queue, array manipulation, and query optimization.

The next part is about system design challenges and practical implementation, and is usually conducted by a staff data engineer. The segment involved a detailed, more technical discussion of your past projects as well. A significant portion of this round involves a system design question related to data pipelines and architecture. During this segment, you’ll need to explain your choices for services and tools, with particular attention to error logging, scalability, and fault tolerance mechanisms. A big data coding challenge may also be presented during this round to challenge you further.

After successfully clearing the technical rounds, candidates proceed to the managerial round, which is conducted by the Senior Engineering Manager, who is typically the hiring manager for the role. This is a discussion-heavy round with you diving deeper into your projects, handling pipeline issues, and demonstrating your decision-making process. The final HR and behavioral round can be combined with this round.

Hiring Committee

The hiring committee reviews feedback from all interviewers, evaluates your technical depth and culture fit, and ensures alignment with PayPal’s leveling criteria. They also consider references when available. Final decisions are made collaboratively to maintain fairness and consistency across all data engineering hires.

PayPal data engineer interview questions typically cover a range of topics including technical coding challenges, system design, and behavioral problem-solving to assess both technical ability and cultural fit.

Coding / Technical Questions

In the technical portion of a PayPal data engineer interview, candidates are expected to solve SQL problems, implement algorithms, and analyze data patterns to demonstrate their hands-on engineering skills:

1. Write a query to get the largest salary of any employee by department

To find the largest salary by department, group the employees by their department using the GROUP BY clause. Then, use the MAX() function to select the maximum salary within each department group.

2. Write a query to get the percentage of comments, by ad, that occurs in the feed versus mentions sections of the app.

To solve this, create two subqueries to count comments in the feed and moments sections for each ad using LEFT JOINs with the ads table. Then, calculate the percentage of comments in each section by dividing the count of comments in each section by the total comments for each ad.

3. Given an (integer) height h and base b, write a function draw_isosceles_triangle that returns the shape of the isosceles triangle using (a 2D list) of 0s and 1s, where 0 and 1 represent the space outside and inside of the triangle, respectively.

To solve this problem, first check if the height h and base b can form a valid isosceles triangle, ensuring b is odd and the level increase is even. Initialize a 2D list filled with zeros, then iterate through each row, calculating the range of indices to fill with ones based on the current level of increase. If the conditions for forming a triangle are not met, return None.

4. Given a table of product subscriptions, write a query to determine if each user has overlapping subscription date ranges

To solve this, use a self-join on the subscriptions table to compare each user’s subscription dates with others. The overlap condition is met if the start date of one subscription is less than or equal to the end date of another, and the end date of the first is greater than or equal to the start date of the second. Group by user_id and use a conditional check to determine if any overlaps exist.

5. Calculate the first touch attribution for each user_id that converted

To determine the first touch attribution, first identify all users who converted by joining the attribution and user_sessions tables and filtering for conversions. Then, find the earliest session for each user using the created_at field to determine the first channel they interacted with. Finally, join this information back to the attribution table to get the channel associated with the user’s first session.

6. Write a query to find the top five paired products and their names

To find paired products often purchased together, join the transactions and products tables to associate transactions with product names. Use a self-join on the combined table to identify pairs of products bought together by the same user at the same time. Ensure the product names are ordered alphabetically to avoid duplicate pairs, and count the occurrences of each pair. Finally, group and order the results to find the top five pairs.

System / Product Design Questions

System design questions in a PayPal data engineer interview focus on evaluating your ability to architect scalable, efficient, and reliable data platforms tailored to real-world business needs:

7. Design a data warehouse for a new online retailer

To design a data warehouse for a new online retailer, start by identifying the business process and assume the warehouse is primarily for analytics and reporting. Define the granularity of sales events, identify dimensions such as buyer, item, date, and payment method, and determine the facts like quantity sold and revenue. Finally, sketch a star schema to represent the design, ensuring it supports efficient querying.

8. Create a schema to keep track of customer address changes

To track customer address changes, design a schema with three tables: Customers, Addresses, and CustomerAddressHistory. The CustomerAddressHistory table records each occupancy period with move-in and move-out dates, allowing you to track both historical and current occupants. This approach uses a Slowly Changing Dimension Type II model to maintain a complete history of address changes and current occupants.

9. Design a solution to store and query raw data from Kafka on a daily basis

To design a cost-effective data analytics solution for storing and querying 600 million daily clickstream events with a two-year retention period, consider using Amazon Redshift for data storage due to its scalability and cost efficiency. Data can be transferred from Kafka to Redshift using Spark Streaming or Amazon Kinesis Data Firehouse, with Spark Streaming being preferred for its cost-effectiveness. The solution should also include a scalable tech stack, a caching layer for latency management, and an orchestrator like Airflow for automation and monitoring.

10. Design a data pipeline for hourly user analytics

To build a data pipeline for hourly, daily, and weekly active user data, start by identifying the necessary data fields and ensuring the table is read-only. Use SQL databases for aggregation, employing queries that utilize the DATE_TRUNC function to group data by hour, day, and week. Implement a unified query approach to aggregate and store data in a data lake, allowing the dashboard to query pre-aggregated data for better performance and scalability. Use an orchestrator like AirFlow to run the process every hour, and consider edge cases like delayed data due to network issues.

11. Migrating a social network’s data from a document database to a relational database for better data metrics

To approach the migration from a document database to a relational database, start by identifying the key entities and their relationships, such as users, friends, and interactions. Normalize the data by creating separate tables for each entity and use foreign keys to establish relationships between them. This will help in reducing data redundancy and improving data consistency, which are common issues in non-normalized document stores. Additionally, consider the specific analytics requirements to ensure the new schema supports efficient querying and reporting.

12. Design a data pipeline to process and aggregate real-time transaction data for fraud detection.

This question assesses your ability to architect scalable pipelines. Describe ingesting data with a streaming platform (e.g., Kafka), processing with Spark or Flink, and storing results in a data warehouse. Explain how you would ensure low-latency and high reliability.

13. Design a storage solution for handling billions of payment logs with fast retrieval for analytics.

Here, interviewers want to see your understanding of distributed storage systems. Propose using a columnar data warehouse (like Snowflake or BigQuery) for analytics, possibly with partitioning and clustering. Discuss trade-offs between cost, speed, and scalability.

Behavioral or Culture-Fit Questions

Behavioral questions in a PayPal data engineer interview aim to understand how you collaborate, adapt, and contribute to the company’s mission through past experiences and decision-making approaches:

14. What are some effective ways to make data more accessible to non-technical people?

At PayPal, data engineers are expected to bridge the gap between technical systems and business teams. To make data more accessible, use well-designed dashboards through tools like Tableau or Looker that enable real-time insights. Create concise documentation tailored to different audiences, such as product managers or risk analysts. Incorporate short walkthrough videos for complex data flows. Most importantly, focus on simplifying metrics and using storytelling techniques to present insights that align with PayPal’s business goals, such as fraud detection or customer behavior trends.

15. Tell me about a project in which you had to clean and organize a large dataset.

In the context of PayPal, large datasets often span terabytes and include high-velocity transactional logs. When answering, describe a project where you processed data at scale—perhaps a pipeline that ingested daily payment logs or fraud signals. Highlight tools relevant to PayPal’s stack, such as Spark, Airflow, or HERA. Explain how you identified data quality issues like duplicates, missing values, or schema mismatches. Emphasize how you implemented validation checks, monitoring, or metadata management to ensure data readiness for downstream consumers like analytics or fraud detection models.

16. Describe a data project you worked on. What were some of the challenges you faced?

Use the STAR method, starting with a project tied to business impact—such as improving the reliability of a data pipeline or building a real-time fraud detection feature. At PayPal, challenges often involve scale, compliance, and coordination across teams. Discuss how you handled performance bottlenecks, reduced data latency, or improved fault tolerance. Include how you collaborated with data scientists or product teams, and conclude with measurable outcomes like reduced pipeline failures, improved SLAs, or increased fraud detection accuracy.

17. How do you stay current with new data engineering technologies and best practices?

Here, interviewers are looking for a proactive learning attitude. Mention resources you use (blogs, courses, conferences), how you experiment with new tools, and how you bring new ideas to your team.

18. How would you ensure your work aligns with PayPal’s mission and values?

This question checks for culture fit and motivation. Discuss how you stay informed about company goals, seek feedback, and incorporate values like customer focus or innovation into your daily work. Give a specific example if possible.

19. Tell me about a time you took ownership of a data pipeline issue and drove it to resolution.

This question seeks evidence of accountability and problem-solving. Use the STAR method: describe the Situation, Task, Actions you took to investigate and fix the issue, and the Result. Emphasize communication and learning outcomes.

Preparing for a data engineer role at PayPal requires a blend of technical mastery, system design expertise, and alignment with the company’s values. The technical foundation should be rooted in strong proficiency with Java and Python, as PayPal data engineers use these languages extensively for backend services, data analysis, and scripting.

SQL skills are absolutely critical, with a particular emphasis on window functions, as PayPal’s systems process billions of SQL queries daily. Practice with MySQL, PostgreSQL, and MongoDB is valuable, as is experience with big data tools like Apache Airflow, Apache Spark (especially PySpark), and Google Cloud Platform services such as BigQuery and Bigtable.

The interview process typically starts with an online assessment focused on coding and SQL, followed by technical rounds that test data structures, algorithms, and practical SQL applications. You should be ready to solve DSA problems like the Rainwater Trap and priority queue challenges, as well as demonstrate advanced SQL techniques, including joins, aggregations, and window functions.

System design interviews will assess your ability to architect scalable data pipelines, handle multiple data sources, and ensure reliability and fault tolerance. PayPal also values cultural fit, so prepare for behavioral questions using the STAR method and our AI Interviewer, highlighting teamwork, adaptability, and alignment with PayPal’s mission of democratizing financial services.

Regular practice with real-world SQL scenarios and mock interviews will help, as will staying current with PayPal’s technology stack and recent innovations. Success hinges on combining technical excellence with clear communication and enthusiasm for PayPal’s vision.

What Is the Average Salary for a Data Engineer Role at PayPal?

Are There PayPal Data Engineer Jobs on Interview Query?

Yes, you can find current PayPal data Engineer jobs on the Interview Query Job Board.

Where Can I Read Discussion Posts on PayPal Tech Interviews?

You can explore our forum threads where candidates share their experiences and insights. Look for posts tagged with PayPal questions for focused discussions.

How Does the PayPal Machine Learning Engineer Interview Differ?

The interview process differs in scope and technical depth. Read our detailed guide on the PayPal machine learning engineer interview for more information.

Preparing for the PayPal data engineer interview is no small feat, but with the right strategy and mindset, success is within reach. The interview process is rigorous and requires depth in SQL, system design, and real-world data pipeline experience. You now have a clear understanding of what to expect and how to prepare.

If you’re mapping out your next steps, start by following our Data Engineer Learning Path that breaks down the key topics you’ll need to master. To dive deeper into what others have faced, explore our curated data engineer questions collection to practice under real interview conditions and read through Alma Chen’s success story to get motivated.