DraftKings Software Engineer candidates report a process centered on coding fundamentals, object-oriented design, project discussion, and behavioral conversations. Several accounts describe HackerRank-based technical work and practical discount or restaurant-design prompts.
$150K
Avg. Base Comp
$183K
Avg. Total Comp
5 rounds
Typical Rounds
4 weeks
Process Length
DraftKings Software Engineer interviews reported here commonly begin with a recruiter conversation, followed by coding work and later conversations that test design, past projects, and team fit. One candidate explicitly described five total rounds, while another described a coding assessment, a live technical interview, and three technical plus two behavioral rounds. Prepare for practical coding and object-oriented design together, rather than treating them as unrelated tracks.
Coding accounts repeatedly mention HackerRank, JavaScript or C#, loops and arrays, and discount-validation scenarios. Candidates were asked to explain runtime, use fast lookups such as dictionaries, and reason about constraints and edge cases. Practice stating your approach clearly as you work; reported prompts may build across multiple parts.
Object-oriented design is a recurring later-stage theme. Candidates described restaurant order-management or robot-kitchen designs that were extended with new robot roles and shared functions. Be ready to draw or explain domain objects, services, relationships, and how the design changes when requirements expand. A project deep dive also appears frequently: candidates discussed architecture, tooling, bottlenecks, fixes, and technical tradeoffs from work they owned.
Behavioral discussions focused on collaboration, mentorship, leadership style, conflict, urgent bugs, and working with teammates who are behind. The evidence is limited to a small set of candidate reports, but it consistently points to a practical, communication-heavy preparation plan.
Synthesized from 7 candidate reports by our editorial team.
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Topics based on recent interview experiences.
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You work as a data scientist for ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
| Question | |
|---|---|
| Why Do You Want to Work With Us | |
| Processing Large CSV | |
| 2nd Highest Salary | |
| Integer to Roman | |
| P-value to a Layman | |
| Google Maps Improvement | |
| Nearest Common Ancestor | |
| Groups of Anagrams | |
| Hurdles In Data Projects | |
| Centralized Event Ingestion | |
| Tower of Hanoi | |
| Valid Anagram | |
| Find Duplicate Numbers in a List | |
| Spam Classifier | |
| Customer Success vs. Free Trial | |
| Track Your Most Valuable Gamers | |
| Matrix Rotation | |
| Worker Distribution Dilemma | |
| Subscription Retention | |
| Video Game Respawn Model | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Concurrent LLM Serving | |
| User Event Data Pipeline | |
| Moving Window | |
| Confidence Interval Explanation | |
| Loan Model | |
| Cloud-Agnostic Deployments | |
| Drink Production Allocation | |
| Deciding Between Solutions |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter or HR discussion covering background, experience, salary expectations, general fit, and next steps. One account specifies a 30-minute recruiter screen; duration may vary by candidate.
Candidates report HackerRank-based coding in JavaScript, Java, or C#. Reported work includes loops, arrays, discount-validation rules, OOP-style prompts, complexity discussion, and multi-part exercises that build on earlier work.
Candidates describe OOAD or system-design sessions involving restaurant management, robot waiters, or a robot kitchen. Interviewers may ask how domain objects, services, movement, and shared functions evolve when new robot roles are added.
Several candidates report discussing a past project in detail, including architecture, external tools, bottlenecks, fixes, technical decisions, and challenges. Prepare a clear narrative for work you personally owned.
Candidates report team-focused conversations with a manager or hiring leader. Topics include leadership style, mentorship, conflict, collaboration, urgent production issues, and how they handle teammates who are behind.
