DoorDash Interview Tips: Answers to Real Questions & OA Interviews
The interview process at DoorDash typically begins with a careful resume screening, focusing on relevant experience and technical skills. Candidates who pass the resume screening are invited to take an Online Assessment (OA), which evaluates coding skills and problem-solving abilities.
Successful OA candidates move on to phone interviews, usually one or two rounds. These initial calls (about 45 minutes in length, sometimes with a 30-45 minute initial screening call) are designed to assess the candidate's basic technical skills, problem solving skills, and to provide an initial look at their fit with the team and culture. Interviews may include real-time programming questions and sometimes system design fundamentals (depending on the requirements of the position), and candidates are required to clearly articulate their thought process.
After a successful phone interview, candidates move on to Onsite Rounds, which typically consist of four to six rounds. Onsite interviews are more difficult and assess a candidate's technical strengths, ability to adapt and innovate, and fit with the company's culture. These interviews cover a variety of areas: programming interviews focus on algorithms and data structures; system design interviews look at scalability and architectural capabilities; and behavioral interviews look at past experiences to see if the candidate is collaborative and aligns with DoorDash's values. The entire on-site interview process was clearly structured and designed to fully assess the skills and potential value of the candidates.
Job Type
SDE
Responsibilities:Responsible for designing, developing, testing and maintaining various systems and services that support DoorDash's core business.
Interviews examine technical points: data structures and algorithms (core), system design (intermediate and advanced), coding skills, object-oriented programming (OOP) principles, testing.
data scientist
Responsibilities:Collect, analyze and interpret massive amounts of data, discover trends and insights, build predictive models, and translate data insights into tangible business results.
Interviews examine technical points: data analysis and modeling, data visualization (Tableau, Looker, Chartio), and coding skills.
product manager
Responsibilities:Responsible for defining product vision and strategy, planning product roadmap, writing detailed product requirement documents and working closely with engineers to ensure products are delivered on time and with high quality.
Interviews examine technical points: product strategy, product design and user experience, technology stack understanding, data analysis skills, project management experience.
The Key Areas of Tech Interview
At DoorDash, it is recommended that you focus on improving your skills in two algorithms, Depth-First Search (DFS) and Breadth-First Search (BFS), which are particularly prominent and common test points. The company also strongly favors Basic Data Structures and Algorithms (Basic DSA) and Advanced Data Structures.
When preparing for these areas, keep in mind that questions related to advanced data structures occur relatively infrequently, but their occasional appearance may present unique challenges. Understanding this nuance in the distribution of questions can help you prepare more strategically, allow you to anticipate the questions you may encounter in an interview, and demonstrate your adaptability and acumen in problem solving.
| Features | Percentage |
|---|---|
| Misc | 9.0% |
| Simulation | 2.2% |
| Two Pointers | 7.9% |
| Adv. Data Structure | 10.1% |
| Backtracking | 6.7% |
| Basic Data Structure and Algorithm | 11.2% |
| Binary Search | 7.9% |
| Heap | 7.9% |
| Graph | 4.5% |
| Dynamic Programing | 4.5% |
| Depth-First Search | 14.6% |
| Breadth-First Search | 13.5% |
Programming interviews at DoorDash are typically of moderate difficulty, similar to what you'd find at a large FAANG Companies may encounter similar situations.
Unlike basic data structure and implementation questions, DoorDash interview questions require more thought and more complex solutions. Like top tech companies, most of the interview questions at DoorDash are moderate to hard.
If you look at the percentage of difficulty of the questions, only 10.71 TP6T were easy questions, the percentage of medium difficulty questions was as high as 51.81 TP6T, and the remaining 37.51 TP6T were high difficulty interview questions.
Questions and Difficulty
| Common Interview Questions | Examining technical points | degree of difficulty or ease |
|---|---|---|
| Designing the file system | Advanced Data Structures | medium difficulty |
| Walls and gates | breadth-first search | medium difficulty |
| The most lucrative assignments | Basic Data Structures and Algorithms, Dichotomous Lookup, Miscellaneous, Double Pointers | medium difficulty |
| Find the closest point with the same X or Y coordinates. | Basic Data Structures and Algorithms | liable (to) |
| Maximizing profit in job scheduling | Dichotomous search, dynamic programming | challenging |
| Minimum number of steps to rearrange two strings | Advanced Data Structures, Heap | medium difficulty |
| The longest incremental path in a matrix | Backtracking, breadth-first search, depth-first search, dynamic programming, graphs | challenging |
| Buddy Sterlings. | Basic Data Structures and Algorithms | liable (to) |
| Checks if a single string swap can equalize strings. | Basic Data Structures and Algorithms | liable (to) |
| Sabu Islands Count | Advanced data structures, breadth-first search, depth-first search | medium difficulty |
DoorDash OA (online assessment)
DoorDash's Online Assessment (OA) is designed to examine the candidate's programming skills and problem-solving abilities, and candidates should focus on reviewing and honing their knowledge of Data Structures and Algorithms (DSAs), particularly common problem patterns like Depth-First Search (DFS) and Breadth-First Search (BFS).
It is also important to have an understanding of advanced data structures, although these types of questions appear less frequently, if encountered they are usually uniquely challenging and a thorough knowledge of these and demonstrating a flexible problem solving mindset will help to stand out in an interview.
SDE
Software Engineers at DoorDash are responsible for building and maintaining the core technologies that underpin the entire distribution ecosystem. This means they face high concurrency, large-scale distributed systems, and complex algorithmic challenges.
Examine the technical points:
- Data Structures and Algorithms (DSA): This is the absolute top priority! Depth-first search (DFS), breadth-first search (BFS), dynamic programming, graph algorithms, sorting, lookups, trees, hash tables, etc. are the basics.DoorDash values your ability to solve real-world programming problems.
- System Design: As the level of the position increases, so do the requirements for system design. You need to be able to design scalable, highly available, high-performance distributed systems, considering aspects such as database selection, message queuing, caching, API design, fault tolerance, and monitoring.
- Programming language proficiency: Your mastery of major programming languages such as Python, Java, Go, Ruby, Scala, etc. and coding habits are usually examined.
- Object-oriented programming (OOP) and software engineering principles: Understand good code structure, modularity and maintainability.
High Frequency Interview Questions:
- Given a list of takeout orders, each containing a starting location and a delivery location, how can an algorithm be designed to plan the optimal route for the delivery person so that he or she can complete all deliveries in the shortest possible time? (May involve graph algorithms, dynamic programming or greedy algorithms)
- Implements a function that identifies the start and end indices of all echo substrings in a sentence.
- How do you design a real-time order matching system that supports processing thousands of orders per second? Consider how to handle latency, failures, and scalability.
- Please design a core system for a takeout platform similar to Uber Eats. What key components, APIs, and how would you ensure the reliability and scalability of the system would you consider?
- Given a map represented by a two-dimensional array containing a start point, an end point, and an obstacle. Find the shortest path from the start point to the end point. (BFS or DFS application)
data scientist
Data Scientists at DoorDash are a key force in leveraging data insights to drive business growth and optimization. They translate complex business problems into data problems and provide solutions through modeling and analytics.
Examine the technical points:
- Statistics and Probability Theory: A/B test design and analysis, hypothesis testing, regression analysis, etc. are fundamental.
- Machine Learning: Proficiency in common machine learning algorithms (e.g., classification, clustering, recommender systems, time series analysis) and understanding of concepts such as model evaluation and feature engineering.
- SQL: Strong SQL querying skills are a must and you need to be able to extract, cleanse and transform data from large databases.
- Programming Languages: Python (Pandas, NumPy, scikit-learn, TensorFlow/PyTorch) and R are the main tools.
- Product analysis and business understanding: Ability to translate data insights into actionable business strategies and evaluate the effectiveness of products or features.
High Frequency Interview Questions:
- Write a SQL query to find the average order value for each restaurant over the past 30 days and display the top five restaurants in descending order of average order value.
- DoorDash has introduced a new feature that allows users to tip delivery drivers before placing an order. How would you design A/B testing to evaluate the success of this feature? What metrics do you need to focus on? If the data results are not favorable, how would you diagnose the problem?
- DoorDash has noticed a significant drop in lunchtime orders in a particular city, how would you as a data scientist investigate this issue? Describe your data analysis steps and possible solutions.
- How do you build a model to predict the demand for takeout in a certain area in the next hour in order to allocate delivery workers more efficiently?
- When performing hypothesis testing, what are Type I and Type II errors? In which case would you consider one of these errors to be more harmful than the other?
product manager
Product Managers at DoorDash act as a bridge between technology, business and users. They need to have a clear product vision, be able to define product requirements, and work closely with the engineering team.
Examine the technical points:
- Product strategy and vision: How to identify market opportunities, define product direction, and develop a product roadmap.
- Technical Understanding: Although not required to write code, a sufficient understanding of the software development lifecycle, system architecture, API integration, data flow, etc. is required to effectively communicate with engineers and assess technical feasibility.
- Data-driven decision making: Ability to leverage data to gain insights into user behavior, measure product success, and perform A/B testing.
- User Experience (UX) and Design Thinking: Understand user pain points and be able to collaborate with designers to ensure a smooth product experience.
- Cross-functional collaboration and communication: Drive different teams (engineering, design, operations, marketing) to work together and clearly communicate product plans and results.
High Frequency Interview Questions:
- You've noticed that DoorDash users have a high rate of cart abandonment during the ordering process. As a product manager, how would you investigate this issue and come up with a solution to optimize the order placement process?
- If DoorDash decided to enter the grocery delivery market, how would you develop a product strategy and roadmap? What features would you prioritize for development?
- You are developing a new payment feature. When working with a team of engineers, what technical questions do you ask them to ensure the project runs smoothly? How will you measure the success of this feature?
- DoorDash has launched a new subscription service in a city. How would you define and track key performance indicators (KPIs) to evaluate the success of this service? If you find that subscriber churn is high, how would you analyze the causes and propose countermeasures?
- Describe the worst user experience a delivery person might encounter when fulfilling a DoorDash order. As a product manager, how would you leverage technology to solve this pain point?
Behavior Questions
What do behavioral interviews talk about? Usually it's just talking about the projects you've worked on!
In an interview, those behavioral questions are really about hearing your story and seeing how you've solved problems and dealt with people in the past so they can probably guess what kind of coworker you'll be in the future.
The classic "Can you do it?" question.
Were there any projects that made you think, "Oops, that's bad, gotta change"? How did you get it done?
- Let's just say: Hey, I'm very flexible and resistant! When I was in a situation like this or that, I quickly understood the situation and pulled my teammates together to find a solution, and in the end, I was able to get it done!
Tell me about a "hardcore" technical problem you've solved.
- Let's just say: Don't look at that problem as difficult, but I rose to the occasion! What exactly was the problem, how did I get involved, what hacks did I use to set it right, and most importantly, how much did my solution benefit the project or team!
Do you have a trick to make a process or system "run faster"? Share one!
- Let's just say: I'm a person who loves to figure out how to do things more efficiently! It was that way, I thought it could be done that way, and then I rolled up my sleeves and did it, and look how much time and resources were saved!
Teamwork. See if you're a good teammate.
Ever worked with a cross-departmental team to make something more efficient together?
- Let's just say: (Think DoorDash delivery and customer service.) I was the lubricant! How did we communicate, how did I help pull the various departments together, and how did it work out in the end?
Were there any projects that were "time-critical and task-intensive" and how did you get through them?
- Let's just say: It was a real rush! First I prioritized, then I managed my time well, and I took advantage of all the help I could get from my team. Sometimes it's a bit of a trick to keep up with the schedule!
Have you ever had one of those "plans don't add up" moments, like when DoorDash suddenly has to change its strategy or discover a new business opportunity? How did you adapt?
- Let's just say: Yes! For example, if you have a program in place, and suddenly new data or a new challenge comes along, you have to make a U-turn. I was the one helping the team make the turn, thinking about how to get to the new goal in the fastest and best way, the same kind of flexibility that DoorDash has!
Talk about your "real skills" for the job.
Were there any sudden big changes in technology and how quickly did you adapt to them?
- Let's just say: I'm a quick learner and adaptable! At that time the situation was like this, I just hurry to charge, hurry to get started, and finally did not also successfully! You see, my adaptability, problem-solving ability and stress resistance, there is nothing to say!
Is there a project where you were the "workhorse" and optimized or redesigned a system to make it better?
- Let's just say: That must have! At that time there was what project came, I was the dominant player, the boss problem to solve, with what technology, how to do step by step, the final effect is simply a qualitative leap, the performance of dilly-dallying up, but also save a lot of money it!
If you were to do it, how would you use your software engineering skills to improve DoorDash's delivery algorithm to minimize delivery time?
- Let's just say: Hmm, that's an interesting question! I'd definitely start by studying where the current algorithm is the bottleneck, and then step by step figure out how to optimize it. Maybe I'll run a simulation test, or AB test or something, to see which solution is the most powerful, anyway, the ultimate goal is to let the user eat the fastest meal!
