Top 22 DoorDash Data Engineer Interview Questions + Guide in 2024

DoorDash is a food-delivery company connecting customers with local restaurants. Being on the front edge of food-delivery services, DoorDash relies highly on skilled Data Engineers for a range of tasks, including managing and optimizing data, making insightful decisions, and enhancing the efficiency of the platform.

If you’re thinking about applying at DoorDash on their data engineering teams, then this guide is for you. We’ll walk you through their process, cover the most frequently asked DoorDash data engineer interview questions, our suggested solutions, and some useful tips to elevate your interview in the eyes of the hiring team.

The DoorDash Data Engineer interview is designed to test your technical abilities, assess your problem-solving skills, and measure how well you align with the company’s culture and values.

There are five different rounds, ensuring a comprehensive assessment of a candidate’s skills and abilities.

Let’s break down the DoorDash Data Engineer interview process step by step:

1. Application

The initial step is submitting your application at the company website, accompanied by an updated resume. You will have access to a portal throughout the application process that provides you with status updates. The hiring team will then review your application to assess your qualifications and background.

2. Initial Interview or Phone Screening

If your resume fits with the requirements of the Data Engineer role, you will be invited to an initial interview or phone screening. You will see your application status updated to “initial screening” or “resume review” in their application portal. This interview will assess your background, qualifications, experience, and the motivation for applying.

3. Technical Assessment Round

Following the phone screen, there will be a technical assessment. This round is crucial since it evaluates your ability to tackle real-world data engineering challenges. This round could involve coding problems, data engineering challenges, or a practical project to evaluate your technical skills. Be prepared to showcase your practical skills during this round.

4. Behavioral Interview

In addition to evaluating your technical skills, DoorDash checks your interpersonal skills to know how well you fit into their culture. In this round, expect questions that explore how you collaborate, communicate, and interact with team members. Be prepared to share previous projects and collaborations, how you handle conflicts, and other scenario-based questions.

5. Final Round

As you progress to the final round, expect questions that assess how well your professional values align with DoorDash’s core values and unique culture. According to their website, they are Leaders, Doers, and Learners, while all pulling for One Team.

After this round, you’ll likely receive feedback on your performance. If successful, there will be more discussions about potential next steps, potential additional interviews with specific team members or leaders, and finally a job offer.

This section provides an overview of commonly asked questions at the DoorDash Data Engineer Interview, covering topics such as behavioral questions, algorithms, data structures, SQL, Python, database design, and ETL processes, which are areas that interviewers often emphasize.

1. Tell me about a time when you went above and beyond expectations in a project. What actions did you take, and how did you achieve results?

This question evaluates your ability to meet and exceed project expectations, which is important for a DoorDash Data Engineer. Interviewers are looking to understand your proactive approach, problem-solving abilities, and the potential impact you can have on project outcomes.

How to Answer

Pick a situation where your actions contributed to the success of a project. Clearly define the challenge you were tasked with, emphasizing the expectations set for the project. Describe the specific actions you took to surpass expectations. Demonstrate the positive impact your actions had on the project’s outcome.

Example

“During my previous role as a Data Engineer, in a data migration project, I identified potential hurdles early on and proposed a restructuring of the ETL process. This not only addressed performance issues but also streamlined data flow, resulting in a 30% reduction in processing time. By proactively collaborating with the team and leveraging my ETL expertise, we not only met expectations but exceeded them, enhancing the overall efficiency of the project.”

2. How will you explain technical challenges to stakeholders who do not have a technical background?

At DoorDash, collaboration between technical and non-technical teams is important, hence this question can be asked to check your ability to bridge the communication gap.

How to Answer

Focus on using plain language, avoiding technical terms, and drill into the impact on business objectives. Describe explaining a high-level overview, emphasizing solutions and outcomes without delving into complex technical details to the stakeholders.

Example

“When communicating technical challenges to non-technical stakeholders, I will prioritize simplicity. For instance, if we’re addressing data processing bottlenecks, I’d draw an analogy, likening it to streamlining the order delivery process in our system for quicker and more efficient results. By framing it in this way, stakeholders will grasp the essence of the challenge and the positive impact on our operations without delving into intricate technicalities.”

3. Tell me about a time when you ensured accuracy in a large dataset, including how you identified and corrected discrepancies, and any preventive measures taken.

To assess your hands-on experience in maintaining data integrity, this query tests your attention to detail, problem-solving skills, and your ability to proactively ensure the accuracy of large datasets. These objectives all align with DoorDash’s commitment to data-driven decision-making.

How to Answer

Focus on a specific instance where your role involved ensuring data accuracy. Clearly outline the steps you took to identify discrepancies, the actions implemented, and any preventive measures you adopted to enhance the overall accuracy of the dataset.

Example

“In my previous role, I was assigned with overseeing a substantial customer database. During routine data quality checks, I noticed discrepancies in customer contact details due to an integration issue. To address this, I conducted a thorough data cleansing process, rectifying inaccuracies and ensuring consistency. As a preventive measure, I implemented regular automated checks and instituted data validation protocols, maintaining the accuracy of the dataset over time.”

4. Tell me about a time when you had to quickly learn and implement a new technology or methodology to address a specific data challenge.

The interviewer asks this question to assess your adaptability. As a Data Engineer at DoorDash, you will encounter various data challenges and will need to adapt to new methods and technologies to overcome them. The interviewer aims to evaluate your agility and problem-solving skills when faced with real-world challenges.

How to Answer

When answering, highlight a specific instance where you encountered a data challenge that required adopting a new technology or methodology. Discuss the steps you took to quickly understand and implement the solution, emphasizing the positive impact on the project or operations.

Example

“At my last job, we had a problem handling data in real time. I saw we needed a better solution, so I learned and used Apache Kafka, a new technology for us. It quickly made our data processing much better. It not only fixed the problem then but also set us up for handling more data in the future, showcasing my ability to swiftly adapt and integrate new technologies for enhanced data processing efficiency.”

5. How do you prioritize tasks and manage your time effectively when working on multiple data engineering projects?

This question assesses your ability to organize and handle multiple projects simultaneously. It evaluates your capacity to prioritize tasks effectively, demonstrating your suitability for the fast-paced and dynamic environment at DoorDash, where simultaneous data engineering projects are frequent.

How to Answer

While answering, share your approach to task prioritization and time management. Emphasize your ability to adapt to shifting priorities and maintain a high standard of work across various projects.

Example

“In my role as a Data Engineer, managing multiple projects at once is common. I usually prioritize tasks by assessing deadlines, project complexity, and potential impact. I use project management tools to create timelines and milestones. Regular check-ins with team members help me stay updated on project progress. Recently, I had two concurrent projects. By prioritizing based on immediate deadlines and complexity, I helped ensure that both were completed successfully. Flexibility is key, allowing me to adapt as project priorities evolve.”

6. Write a query for the user_experiences table to find the percentage of users who transitioned directly from the title of “Data Analyst” to “Data Scientist”, with no other titles in between.

This question tests your SQL querying skills and ability to extract meaningful insights from a database. It assesses your understanding of data transitions, and the capability to solve complex queries, again aligning with DoorDash’s focus on data-driven decision-making.

How to Answer

Write a SQL query that selects users who transitioned directly from “Data Analyst” to “Data Scientist”, with no other titles in between. Utilize subqueries or join conditions to capture the specific data pattern, and calculate the percentage based on the total number of users.

Example

“First I would write an SQL query that selects users from the user_experiences table who transitioned directly from the title “Data Analyst” to “Data Scientist” with no other titles in between. I’d use a combination of joins and conditions, ensuring that the timestamp of the “Data Analyst” entry is earlier than the timestamp of the “Data Scientist” entry. To exclude cases where users had additional titles in between, I’d utilize a LEFT JOIN and filter out those instances with a WHERE clause. Finally, I’d calculate the percentage by dividing the count of distinct users meeting the criteria by the total count of distinct users.”

7. Write a query to find the IDs of wines in the wines table that meet a customer’s criteria: Alcohol content greater or equal to 13%, ash content less than 2.4, and color intensity less than 3.

This question tests your SQL proficiency and your ability to write queries that meet specific criteria. This task emphasizes data extraction and analysis, essential skills for managing and optimizing large datasets within the context of a food delivery and logistics platform like DoorDash.

How to Answer

Create a SQL query that selects wine IDs from the wines table based on the customer’s criteria. Use the WHERE clause to filter entries according to the specified conditions.

Example

“To find wines that match the customer’s criteria, I would write a SQL query like this: ‘SELECT id FROM wines WHERE alcohol_content >= 13 AND ash_content < 2.4 AND color_intensity < 3;’. This query selects the IDs from the wines table where the alcohol content is 13% or greater, ash content is less than 2.4, and color intensity is less than 3. It precisely filters the data to provide a list of suitable wines for the customer.”

8. Write a query to retrieve the total number of vacation bookings in the last 90 days, last 365 days, and all time from a table.

During a DoorDash Data Engineer interview, this question assesses your proficiency in SQL querying and your capacity to manipulate time-based data. It assesses your comprehension of date functions and filtering within SQL, in line with the requirement for analyzing temporal data.

How to Answer

Compose a SQL query that calculates the total bookings within the specified time frames using appropriate date functions. Utilize the date (January 1, 2022) as a reference point for the calculations.

Example

“To retrieve the total number of vacation bookings within the specified time frames, I would craft a SQL query like this: ‘SELECT COUNT() AS total_last_90_days, COUNT() AS total_last_365_days, COUNT(*) AS overall FROM vacation_bookings WHERE booking_date >= ‘2021-10-04’;‘. This query uses the COUNT function to calculate the total bookings in the last 90 days, last 365 days, and overall, considering today as January 1, 2022. It effectively filters the data based on the booking date to provide the required counts.”

9. Given an array of integers, write a function move_zeros_back that moves all zeros in the array to the end of the array. If there are no zeros, return the input array.

This question could be asked to see how well you can solve a specific coding problem. Being a Data Engineer at DoorDash, you will often encounter similar problems when dealing with optimizing data pipelines. Hence, the interviewer aims to assess your approach to problem-solving and your ability to optimize data structures.

How to Answer

Write a function in a programming language of your choice that takes an array of integers as input and moves all zeros to the end of the array. Ensure the function handles various cases, including scenarios where there are no zeros in the array.

Example

“To move zeros to the end of the array, I would create a function in Python. First, I’d filter out non-zero elements and count the number of zeros in the array. Then, I’d construct a new array by appending the non-zero elements followed by the required number of zeros. If there are no zeros, I’d return the input array unchanged. This approach ensures the zeros are moved to the end while maintaining the original order of non-zero elements.”

10. Explain the backpropagation algorithm in neural networks, its informal intuition, and mention drawbacks compared to other optimization methods.

As a Data Engineer at DoorDash, you may encounter projects where you have to implement machine learning solutions to address complex challenges such as optimizing delivery routes. The interviewer tests your understanding of basic machine learning concepts as they directly influence your ability to effectively contribute to such projects.

How to Answer

Provide a concise yet comprehensive explanation of the backpropagation algorithm, its informal intuition, and discuss any drawbacks it may have in comparison to alternative optimization methods.

Example

“The backpropagation algorithm in neural networks is a method for adjusting model weights based on the error in predictions during training. Informally, it involves iteratively fine-tuning weights to minimize prediction errors. However, drawbacks include sensitivity to initial weights, potential for vanishing/exploding gradients, and the need for large labeled datasets. Compared to some optimization methods, backpropagation may face challenges in convergence speed and generalization.”

11. What is the difference between clustered index and non clustered index?

This question tests your knowledge of database management systems. Understanding the difference between clustered and non-clustered indexes is crucial for optimizing database performance, a key aspect of data engineering tasks at DoorDash.

How to Answer

Differentiate between clustered and non-clustered indexes by highlighting their core characteristics, use cases, and the impact on database performance. Consider mentioning scenarios where one might be preferred over the other based on specific requirements.

Example

“A clustered index determines the physical order of data rows in a table based on the indexed column. In contrast, a non-clustered index organizes a separate structure that points to the actual data rows. While a table can have only one clustered index, multiple non-clustered indexes are allowed. Clustered indexes are generally beneficial for range queries and sequential access, but may cause fragmentation. Non-clustered indexes, on the other hand, offer flexibility but involve additional lookup operations. Choosing between them depends on factors like query patterns, write performance, and data distribution.”

12. What is a load table and why do we need it in a SharePoint server?

This question assesses your knowledge of data handling in SharePoint, a critical skill for Data Engineers who manage large datasets and ensure smooth operations in a food delivery and logistics platform like DoorDash.

How to Answer

Explain what a load table is in the context of SharePoint, emphasizing its purpose and significance in managing data efficiently. Consider discussing scenarios or use cases where the use of a load table becomes crucial.

Example

“A load table in SharePoint is a structured storage space crucial for efficiently handling and managing large volumes of data during processes like data migrations or initial setups. It optimizes performance, minimizes errors, and ensures organized data integration into the SharePoint environment.”

13. How would you design an ETL pipeline for collecting and aggregating unstructured multimedia data from videos for a model?

This question is commonly asked at DoorDash Data Engineer interviews as it assesses your proficiency in designing a system (ETL pipeline) to organize unstructured multimedia data from videos. Your answer will demonstrate your comprehension of managing intricate data, which is important for enhancing DoorDash’s data operations.

How to Answer

When answering, provide a concise yet comprehensive overview of how you would design an ETL pipeline to collect and aggregate unstructured multimedia data from videos. Consider discussing the steps involved, tools used, and techniques you would employ.

Example

“In designing the ETL pipeline, I would first implement a data collection mechanism to extract relevant information from videos, using tools like OpenCV or FFmpeg. Then, I’d preprocess the data to extract features and transform it into a structured format. For aggregation, I will use a distributed processing framework like Apache Spark to handle large-scale data. The final step will involve loading the processed data into a suitable storage system for model consumption, such as a data warehouse or cloud storage. This will ensure a streamlined flow from raw multimedia data to structured, aggregated information for model training.”

14. Write a function to compute the recency-weighted average salary of a data scientist based on the provided list of salaries from the past ‘n’ years.

This question tests your proficiency in creating a function that calculates a weighted average, showcasing your understanding of data analysis and computational skills necessary for the Data Engineer role at DoorDash.

How to Answer

Provide a concise function in your programming language of choice that calculates the recency-weighted average of data scientist salaries based on the given list. Explain the logic behind your function, emphasizing the application of linear recency weighting.

Example

“To compute the recency-weighted average salary of a data scientist, I would create a function in Python named ‘recency_weighted_average’. Iterating through the list of salaries, I’d assign higher weights to more recent years using a linear recency weighting scheme. The weighted sum of salaries is then divided by the sum of the weights, providing the recency-weighted average. I would round the result to two decimal places for accuracy. This function ensures that recent salaries contribute more to the average, aligning with the recency weighting requirement.”

15. Write a function pick_host to determine the friend with the optimal location to host a party for a group of N friends, each represented by a dictionary with name and 3D coordinates (x, y, z).

This question can be asked to test your proficiency in spatial data and computation, showcasing your problem-solving skills in practical scenarios, which aligns with the real-world challenges often encountered in data engineering roles at DoorDash.

How to Answer

While answering, provide a concise function in your programming language of choice that calculates the optimal host based on the least travel distance for the group. Explain the logic behind your function, emphasizing the consideration of 3D coordinates.

Example

“To determine the optimal host for the party, I’d create a function in Python named ‘pick_host’. I would iterate through the list of friends, calculate the Euclidean distance for each friend using their 3D coordinates, and keep track of the friend with the minimum distance. The friend with the minimum distance would be considered the optimal host, ensuring the least travel distance for the group. This function accounts for the spatial layout of friends and selects the one whose location minimizes the overall travel effort for the group.”

16. Write a function ‘min_distance’ to find pairs of elements with the minimum absolute distance in an array of integers, returning them in ascending order.

In the DoorDash Data Engineer interview, this question evaluates your ability to utilize data for UI enhancement, testing your understanding of user journey analysis in a real-world data engineering context.

How to Answer

Provide a concise function in your programming language of choice that calculates the minimum absolute distance between elements and returns pairs meeting the criteria. Explain the logic behind your function, emphasizing the consideration of absolute differences and sorting.

Example

“To find pairs with the minimum absolute distance in an array, I’d create a function in Python called ‘min_distance’. First, I would sort the array to simplify the calculation. Then, I’d iterate through the sorted array, calculating the absolute difference between each element and its adjacent one. I’d find the minimum absolute distance among these pairs. Next, I’d iterate again to identify pairs with this minimum distance, creating a list of such pairs. Finally, I’d return this list of pairs in ascending order, ensuring a clear and organized output. This function considers both sorting and absolute differences to efficiently find and present pairs meeting the specified criteria.”

17. Write a function ‘plan_trip’ to reconstruct the order of layovers in a trip given a list of out-of-order flight tickets each with a starting city and end city.

This question can be asked to test your coding and problem-solving skills in organizing and structuring data, mirroring challenges faced in real-world scenarios at DoorDash where optimizing and reconstructing routes for efficient logistics are crucial components of data engineering tasks.

How to Answer

Provide a concise function in your programming language of choice that reconstructs the order of layovers using the provided list of out-of-order flight tickets. Explain the logic behind your function, emphasizing the sequencing aspect.

Example

“To reconstruct the layover order in a trip, I’d create the function in Python ‘plan_trip.’ Utilizing a dictionary, ‘ticket_dict,’ I’d map starting cities to their corresponding end cities based on the provided out-of-order flight tickets. Identifying the starting city involves finding the one without a matching end. The trip path is then iteratively built by appending tuples for each layover, starting from the initial city and progressing to the next based on the mapping in ‘ticket_dict.’ This process continues until the last city is reached, resulting in a well-organized sequence of layovers in the reconstructed trip tickets.”

18. Suppose you are given access to user event data tables, how would you analyze the data to recommend UI improvements?

In a DoorDash Data Engineer interview, this question evaluates your ability to utilize data for UI enhancement, testing your understanding of user journey analysis in a real-world data engineering context.

How to Answer

Detail the specific analyses you’d undertake, such as tracking user interactions, identifying patterns, and evaluating key metrics. Explain how these analyses would inform actionable recommendations for UI changes.

Example

“I would conduct a comprehensive user journey analysis using the available tables summarizing user event data for our app. This analysis would involve tracking various user interactions, identifying patterns in user behavior, and delving into key engagement metrics such as page views, click-through rates, and time spent on different app features. This analysis would guide informed UI enhancements, ensuring a more user-friendly experience.”

19. Let’s say you’re a Data Engineer on the marketing team for DoorDash. How would you determine how much DoorDash should pay for an ad in a third-party app?

To test your ability to apply data-driven strategies in a marketing context, this question evaluates your understanding of data analysis and decision-making to optimize advertising expenditures, valuable skills for the Data Engineer role at DoorDash.

How to Answer

Outline a methodical approach, mentioning key data points like user demographics, app engagement metrics, and historical performance data. Emphasize the importance of analyzing conversion rates and customer acquisition costs to make informed decisions.

Example

“As a DoorDash Data Engineer in the marketing team, I’d analyze user demographics and historical ad performance on the third-party app. Considering app engagement metrics, I’d prioritize KPIs like conversion rates and customer acquisition costs. This data-driven approach ensures DoorDash pays optimally, aligning with the app’s user base and maximizing advertising ROI.”

20. Suppose you’re running a SQL query, and it’s taking a while. How do you determine if it’s too slow, and what steps would you take to improve efficiency?

This question tests your ability to troubleshoot SQL performance issues, your understanding of indicators of slow queries, and your approach to optimizing query efficiency.

How to Answer

Explain how you’d check query execution time, use database profiling tools, and analyze query plans. Mention specific optimization techniques such as indexing and rewriting queries for better performance, critical skills for a Data Engineer role at DoorDash.

Example

“If a SQL query seems slow, I’d check its execution time and use profiling tools to identify bottlenecks. Analyzing query plans helps to pinpoint inefficiencies. I’d consider indexing and, if needed, rewrite the query to improve efficiency and ensure optimal database performance.”

21. You are tasked to build a machine-learning system that minimizes missing or wrong orders placed on the app. How would you go about designing this system?

This question assesses the candidate’s understanding of data pipelines, machine learning model development, and the integration of these models into the operational workflow to improve order accuracy. It also examines the candidate’s problem-solving skills, their knowledge of feature engineering, data quality, and their ability to work cross-functionally with other teams.

How to Answer

Begin by emphasizing the importance of data collection and preprocessing, focusing on order details, customer information, historical accuracy data, and timestamps. Mention the selection of a suitable machine learning model, like a classification algorithm, trained and validated using historical data. Explain the creation of a feedback loop for continuous model improvement with new data.

Example

Here’s a helpful example of how to answer this particular question.

22. You’re given all the different marketing channels along with their respective marketing costs. What metrics would you use to determine the value of each marketing channel?

This tests the candidate’s understanding of key performance metrics and their ability to apply data engineering skills to real-world business problems.

How to Answer

Our key metric is our marketing ROI (revenue over expenses) with respect to each of our marketing channels. Knowing our key metric, we can start drilling down into first- and second-level metrics.

Example

“Our key metric is marketing ROI, calculated as revenue over expenses for each marketing channel. To break this down, we focus on two main metrics: Customer Lifetime Value (CLV) and Customer Acquisition Cost (CAC). CLV measures the revenue from each customer over time, calculated as the average revenue per customer (ARPC) divided by the churn rate. CAC measures the cost of acquiring a new customer, including marketing and sales expenses. By analyzing these metrics, we can determine the effectiveness and value of each marketing channel.”

Here are some useful tips to help you prepare for a Data Engineer interview at DoorDash:

1. Understand DoorDash Culture and Tech Stack

Explore DoorDash’s organizational culture and technology stack from their website and other sources, including databases, data processing tools, and any specific technologies employed. This comprehensive understanding will not only provide insights into the tools you could be utilizing, but also demonstrate the company’s values and practices.

After getting familiar with DoorDash’s tech stack, you may practice and enhance your skills through Interview Query’s learning paths where we offer  a range of resources designed to sharpen your problem-solving abilities, algorithmic thinking, and coding proficiency.

2. Review SQL and Programming Concepts

Brush up on your SQL skills, as it’s a fundamental requirement for data engineering roles. Be prepared to write queries, understand complex joins, and optimize queries for performance. Be proficient in programming languages commonly used in data engineering, such as Python, Java, or Scala, and focus on Data Structure and algorithms.

You can practice SQL programming and data structure questions through our Interview Questions at Interview Query.

3. Focus on Real-world Problem-Solving

Be ready to tackle real-world scenarios and problem-solving exercises. DoorDash might present you with data-related challenges to assess your problem-solving skills.

To practice, you can try out our Challenges feature at Interview Query, where you can practice a variety of data engineering-related challenges to sharpen your problem-solving abilities.

4. Practice Behavioral Questions

Prepare for behavioral questions that could be asked to assess your ability to work in a team, communicate effectively, and handle challenging situations.

To enhance your preparation, try out our Interview Question database. It provides a range of behavioral questions to practice, allowing you to refine your responses and build confidence in addressing various scenarios.

5. Mock Interviews

Lastly, practice mock interviews with a friend or online. This can help you get accustomed to articulating your thoughts clearly and receiving feedback on your responses.

Boost your confidence by engaging in real-time mock interviews with like-minded peers through our Mock Interviews feature at Interview Query.

What is the average salary for a Data Engineer role at DoorDash?

The average base salary for a Data Engineer at Doordash is $183,357 based on 7 data points. Adjusting the average for more recent salary data points, the average recency-weighted base salary is $183,673.

Apart from DoorDash, which other companies can I apply to as a Data Engineer?

There are a lot of opportunities in various industries for Data Engineers. You can apply to tech giants like Google and Amazon, financial institutions such as JPMorgan, and innovative startups like Airbnb. Diverse sectors like healthcare, with Pfizer, and technology, with IBM, also seek Data Engineers.

You can check out our Company Interview Guide to find out more about other companies and their interview processes.

Are there job postings at Interview Query for DoorDash Data Engineer position?

Currently, we don’t have specific job postings for the DoorDash Data Engineer position at Interview Query. However, we consistently update our Job Board with open vacancies from various tech companies, providing a diverse range of opportunities for data engineering roles.****

In conclusion, acing the DoorDash Data Engineer interview involves honing your technical skills, coding & problem-solving abilities, and fitting into the company culture.

If you want to learn more, consider heading over to our main DoorDash guide. We’ve provided additional information there, covering relevant interview questions for different positions such as Data Analyst, Scientist, and Software Engineer.

If you’re looking for more interview guides for Data Engineers as a whole, we also have SQL and Behavioral guides. You can also check out our main Data Engineer Interview Questions, just to make sure you don’t miss anything important.

With dedicated preparation, ongoing learning, and a focus on all the areas described above, you’ll be well-equipped to ace your interview. We hope for your success and want you to know that Interview Query is here to assist you with every step of the application process at DoorDash!