Draftkings Business Intelligence Interview Guide

Getting ready for a Business Intelligence interview at DraftKings? The DraftKings Business Intelligence interview process typically spans a range of question topics and evaluates skills in areas like data pipeline design, dashboard creation, stakeholder communication, data modeling, and deriving actionable insights from complex datasets. Excelling in this interview requires a deep understanding of how to transform raw data into clear, impactful reports and visualizations that drive business decisions, as well as the ability to explain technical concepts to both technical and non-technical audiences in a fast-paced, data-driven environment.

In preparing for the interview, you should:

• Understand the core skills necessary for Business Intelligence positions at DraftKings.
• Gain insights into DraftKings’ Business Intelligence interview structure and process.
• Practice real DraftKings Business Intelligence interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the DraftKings Business Intelligence interview process, along with sample questions and preparation tips tailored to help you succeed.

DraftKings is a leading digital sports entertainment and gaming company that offers daily fantasy sports, sports betting, and online casino products to millions of users across North America and select international markets. The company leverages advanced technology and data analytics to deliver engaging, personalized gaming experiences and drive responsible play. As a Business Intelligence professional at DraftKings, you will play a crucial role in transforming data into actionable insights, supporting strategic decision-making, and enhancing the company’s competitive edge in the dynamic online gaming industry.

As a Business Intelligence professional at DraftKings, you are responsible for gathering, analyzing, and interpreting data to support strategic decision-making across the organization. You will work closely with product, marketing, and operations teams to develop dashboards, generate actionable insights, and identify trends that drive business growth. Your role involves building and maintaining data models, ensuring data accuracy, and presenting findings to stakeholders to inform product enhancements and user engagement strategies. This position is integral to optimizing DraftKings’ offerings and supporting its mission to deliver an innovative and data-driven gaming experience.

2.1 Stage 1: Application & Resume Review

The process begins with a comprehensive review of your application and resume by the DraftKings talent acquisition team. They assess your professional background for business intelligence experience, proficiency in data modeling, dashboard development, ETL pipeline design, and effective stakeholder communication. Emphasis is placed on your ability to translate complex data into actionable insights and your familiarity with data warehousing and analytics systems. To prepare, ensure your resume clearly highlights relevant technical and business-facing skills, as well as measurable impacts from previous roles.

2.2 Stage 2: Recruiter Screen

A recruiter will reach out for an initial phone conversation, typically lasting 20–30 minutes. This stage focuses on your motivation for joining DraftKings, your understanding of the business intelligence function, and a high-level review of your experience with data visualization, reporting, and cross-functional collaboration. Expect questions about your interest in the gaming and sports entertainment industry, and be prepared to discuss your approach to communicating insights to non-technical audiences. Preparation should include a succinct narrative about your career trajectory and alignment with DraftKings’ mission.

2.3 Stage 3: Technical/Case/Skills Round

This stage generally consists of one or two interviews led by business intelligence team members or a hiring manager. You’ll be evaluated on your technical depth in SQL, Python, data pipeline architecture, dashboard creation, and the design of scalable reporting solutions. Case studies or whiteboard exercises may cover topics such as designing a data warehouse for a new product, building an ETL pipeline, or developing a solution for real-time user analytics. You may also be asked to interpret clickstream or transactional data, address data quality issues, or propose metrics for business experiments. Preparation should focus on articulating your problem-solving process and demonstrating hands-on expertise with the tools and techniques mentioned in your resume.

2.4 Stage 4: Behavioral Interview

A behavioral interview, often conducted by a cross-functional stakeholder or business intelligence leader, explores your collaboration style, adaptability, and communication skills. You’ll be asked to describe past experiences overcoming challenges in data projects, resolving misaligned stakeholder expectations, and making data accessible to diverse audiences. Emphasis is placed on your ability to present complex insights clearly and drive business impact through data-driven decision-making. Prepare by reflecting on key projects where you navigated ambiguity or ensured data quality, and practice framing your stories using the STAR method.

2.5 Stage 5: Final/Onsite Round

The final round typically involves a series of interviews with business intelligence team members, product managers, and occasionally executives. This stage may include a technical presentation, a deep-dive into a case study, and additional behavioral questions. You’ll be expected to showcase your end-to-end abilities—from designing robust data pipelines to crafting executive-level dashboards and reports. You may also need to demonstrate how you would communicate insights to stakeholders with varying technical backgrounds. Preparation should center on synthesizing your technical and business acumen, and being ready to discuss recent trends in business intelligence relevant to DraftKings.

2.6 Stage 6: Offer & Negotiation

After successful completion of the interview rounds, you’ll connect with the recruiter to discuss the offer package, including compensation, benefits, and start date. This stage allows for negotiation and clarification of role expectations. Preparation here involves researching industry standards and being ready to advocate for your value based on your experience and the scope of the role.

2.7 Average Timeline

The typical DraftKings Business Intelligence interview process spans 3–5 weeks from initial application to offer. Fast-track candidates with highly relevant experience or internal referrals may progress in as little as 2–3 weeks, while standard pacing involves a week between each stage, subject to team availability and scheduling. The technical/case round may require a take-home assignment with a 3–5 day deadline, and onsite rounds are usually scheduled within a week of completion of prior interviews.

Next, let’s dive into the types of interview questions you can expect throughout this process.

Below are sample interview questions you may encounter for a Business Intelligence role at DraftKings. These questions will assess your technical, analytical, and communication skills, as well as your ability to design scalable data solutions and present actionable insights. Focus on demonstrating your proficiency in data modeling, dashboarding, stakeholder communication, and your ability to translate complex data into business value.

3.1 Data Modeling & Warehousing

Questions in this section evaluate your ability to design robust data models and warehouses that support scalable analytics and reporting. Expect to discuss data architecture decisions, ETL processes, and how to ensure data integrity in complex environments.

3.1.1 Design a data warehouse for a new online retailer
Explain your approach to schema design, fact and dimension tables, and how you would support efficient querying for business stakeholders. Highlight normalization, scalability, and how you’d handle slowly changing dimensions.

3.1.2 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data
Describe the end-to-end pipeline, including error handling, data validation, and methods for ensuring high data quality. Discuss choices of tools and how to automate recurring ingestion.

3.1.3 Design a solution to store and query raw data from Kafka on a daily basis
Outline how you would architect storage and querying for large-scale streaming data, focusing on partitioning, retention, and efficient downstream analytics.

3.1.4 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners
Discuss how you would handle schema variability, monitoring, and error recovery. Emphasize data mapping, transformation logic, and maintaining data consistency.

3.2 Data Quality & ETL

This section covers your strategies for ensuring data accuracy, reliability, and consistency, especially in complex or high-volume environments. Be prepared to talk about troubleshooting, automation, and quality assurance.

3.2.1 How would you approach improving the quality of airline data?
Walk through your process for profiling, cleaning, and monitoring data. Include approaches for identifying root causes and implementing preventive measures.

3.2.2 Write a query to get the current salary for each employee after an ETL error
Demonstrate your ability to identify and correct data discrepancies using analytical SQL. Explain how you’d verify correctness post-fix.

3.2.3 Let's say that you're in charge of getting payment data into your internal data warehouse.
Detail your approach to ingestion, validation, and reconciliation. Discuss how you’d ensure data timeliness and integrity.

3.2.4 Ensuring data quality within a complex ETL setup
Describe how you would monitor, test, and document complex ETL processes to guarantee reliable reporting and analytics.

3.3 Experimentation & Metrics

These questions focus on your ability to define, track, and interpret key business metrics and experiments. You’ll need to show both technical and business acumen in connecting data to outcomes.

3.3.1 The role of A/B testing in measuring the success rate of an analytics experiment
Explain how you would design, execute, and analyze an A/B test, including metric selection and statistical significance.

3.3.2 You work as a data scientist for a 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?
Describe your experimental design, control groups, and which KPIs (e.g., retention, revenue, customer acquisition) you’d monitor.

3.3.3 User Experience Percentage
Show how you’d define and calculate user experience metrics, and how these insights would inform product or business decisions.

3.3.4 What kind of analysis would you conduct to recommend changes to the UI?
Discuss your approach to mapping user journeys and identifying friction points using data, and how you’d translate findings into actionable recommendations.

3.4 Data Communication & Visualization

This section evaluates your ability to communicate complex data clearly to diverse audiences and make insights actionable for business stakeholders. Focus on storytelling, visualization, and tailoring your message.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Describe structuring your presentation, simplifying visuals, and adjusting your message based on stakeholder needs.

3.4.2 Making data-driven insights actionable for those without technical expertise
Share strategies for breaking down complexity, using analogies, and focusing on business impact.

3.4.3 Demystifying data for non-technical users through visualization and clear communication
Explain how you design dashboards or reports to maximize accessibility and drive decision-making.

3.4.4 Strategically resolving misaligned expectations with stakeholders for a successful project outcome
Walk through a framework for managing stakeholder communication, resetting expectations, and ensuring alignment.

3.5 Business Problem Solving & Analytics

These questions test your ability to approach open-ended business problems, design analytics solutions, and connect your work to business outcomes.

3.5.1 Describing a data project and its challenges
Discuss a specific project, the obstacles you faced, and how you overcame them using both technical and soft skills.

3.5.2 You're analyzing political survey data to understand how to help a particular candidate whose campaign team you are on. What kind of insights could you draw from this dataset?
Outline your approach to extracting actionable insights, segmenting voters, and identifying key drivers of support.

3.5.3 How would you analyze how the feature is performing?
Describe using funnel analysis, cohort analysis, or other metrics to measure feature adoption and effectiveness.

3.5.4 How would you approach the business and technical implications of deploying a multi-modal generative AI tool for e-commerce content generation, and address its potential biases?
Explain your approach to evaluating business value, technical feasibility, and ethical considerations such as bias mitigation.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Highlight how you identified a business problem, analyzed relevant data, and influenced an outcome. Emphasize the impact your recommendation had on the business.

3.6.2 Describe a challenging data project and how you handled it.
Discuss the complexities involved, how you structured your approach, and the problem-solving skills you used to deliver results.

3.6.3 How do you handle unclear requirements or ambiguity?
Share your process for clarifying objectives, iterating quickly, and keeping stakeholders aligned throughout the project.

3.6.4 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Describe the situation, how you adjusted your communication style, and the outcome of your efforts.

3.6.5 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a dashboard quickly.
Explain your prioritization framework and how you managed trade-offs between speed and quality.

3.6.6 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Focus on your persuasion skills, use of evidence, and how you built consensus.

3.6.7 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Walk through your investigation process, validation steps, and how you resolved the discrepancy.

3.6.8 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
Share your time management strategies, tools you use, and how you communicate priorities with your team.

3.6.9 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Discuss your approach to handling missing data, transparency with stakeholders, and the business value delivered despite limitations.

3.6.10 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Describe the automation tools or scripts you implemented and the impact on team efficiency and data reliability.

4.1 Company-specific tips:

• Dive into DraftKings’ business model and core offerings, including fantasy sports, sports betting, and online casino products. Understand the regulatory environment and how data influences user acquisition, retention, and responsible gaming.
• Research recent product launches, partnerships, and technology initiatives at DraftKings. Be ready to discuss how business intelligence can drive growth, improve user experience, and inform strategic decisions in a rapidly evolving market.
• Familiarize yourself with the unique challenges of the online gaming industry, such as real-time data processing, fraud detection, and compliance reporting. Consider how business intelligence teams contribute to solving these challenges.

4.2 Role-specific tips:

4.2.1 Master designing robust data pipelines and data models that support scalable analytics.
Practice articulating your approach to building ETL pipelines that ingest heterogeneous data sources—such as clickstream, transactional, and third-party partner data. Be ready to discuss best practices for data validation, error handling, and schema design, especially in high-volume environments like DraftKings. Highlight your experience with tools and frameworks for automating ingestion and ensuring data consistency across the organization.

4.2.2 Showcase your ability to create impactful dashboards and reports for diverse stakeholders.
Prepare examples of dashboards you’ve built that track key business metrics, user engagement, and product performance. Focus on how you tailor visualizations to different audiences—executives, marketing, product, and operations teams—making complex insights accessible and actionable. Practice explaining your design choices and the rationale behind metric selection.

4.2.3 Demonstrate expertise in data quality management and troubleshooting complex ETL setups.
Be ready to walk through your process for profiling, cleaning, and monitoring data to ensure accuracy and reliability. Discuss how you identify root causes of data quality issues, implement preventive measures, and automate recurrent data-quality checks. Share examples of how you resolved discrepancies between source systems or overcame data reliability challenges under tight deadlines.

4.2.4 Prepare to discuss experimentation, KPI selection, and business impact measurement.
Practice explaining how you design and analyze A/B tests, select meaningful KPIs, and interpret results to drive business decisions. Be ready to describe your approach to measuring the impact of promotions, product changes, or new features—connecting data-driven insights to tangible business outcomes.

4.2.5 Refine your communication skills for presenting complex insights to non-technical stakeholders.
Develop clear frameworks for structuring presentations, simplifying visualizations, and adjusting your message based on the audience’s technical proficiency. Use analogies and real-world examples to demystify data, focusing on business impact and actionable recommendations. Prepare stories that highlight your ability to bridge the gap between technical and business teams.

4.2.6 Practice business problem-solving using open-ended scenarios relevant to DraftKings.
Think through how you would approach ambiguous business problems, such as optimizing user journeys, recommending UI changes, or analyzing the effects of a new feature. Structure your responses to demonstrate both technical rigor and strategic thinking, always tying your analysis back to business objectives.

4.2.7 Be ready to share behavioral examples that showcase adaptability, stakeholder influence, and prioritization.
Reflect on past experiences where you navigated unclear requirements, managed multiple deadlines, or influenced stakeholders without formal authority. Use the STAR method to frame your stories, emphasizing your collaboration style, resilience in the face of challenges, and ability to deliver business value under pressure.

4.2.8 Highlight your experience balancing speed and data integrity in high-stakes environments.
Prepare to discuss trade-offs you’ve made between shipping dashboards quickly and maintaining long-term data quality. Explain your prioritization framework and how you communicate these decisions to stakeholders, ensuring transparency and alignment.

4.2.9 Illustrate your approach to handling messy or incomplete data while delivering actionable insights.
Share examples of projects where you worked with datasets containing nulls or inconsistencies. Discuss your analytical trade-offs, transparency with stakeholders, and the business impact of your findings despite data limitations.

4.2.10 Demonstrate your commitment to continuous improvement and automation in data processes.
Talk about how you’ve automated recurrent data-quality checks or reporting tasks to prevent future crises and enhance team efficiency. Highlight the tools, scripts, or workflows you implemented and the measurable improvements achieved.

5.1 How hard is the DraftKings Business Intelligence interview?
The DraftKings Business Intelligence interview is challenging and comprehensive, designed to assess both technical and business acumen. You’ll be tested on your ability to design robust data pipelines, build scalable dashboards, communicate insights to diverse stakeholders, and solve real-world business problems. Expect a fast-paced environment and questions that require you to connect data analysis directly to DraftKings’ gaming and sports entertainment business objectives. Candidates who excel at translating raw data into actionable insights and can clearly articulate their problem-solving approach have a distinct advantage.

5.2 How many interview rounds does DraftKings have for Business Intelligence?
DraftKings typically conducts 4–5 interview rounds for Business Intelligence roles. The process starts with a recruiter screen, followed by technical and case interviews, a behavioral round, and a final onsite or virtual panel. Each stage is designed to evaluate a different facet of your expertise, from technical skills and data modeling to stakeholder communication and cultural fit.

5.3 Does DraftKings ask for take-home assignments for Business Intelligence?
Yes, DraftKings often includes a take-home assignment as part of the technical or case interview round. The assignment usually involves building a dashboard, designing an ETL pipeline, or analyzing a complex dataset to derive actionable business insights. You’ll typically have 3–5 days to complete it, and it’s your opportunity to showcase hands-on skills and creativity in solving real DraftKings business challenges.

5.4 What skills are required for the DraftKings Business Intelligence?
Key skills for DraftKings Business Intelligence professionals include advanced SQL, data modeling, ETL pipeline design, dashboard creation (often with tools like Tableau or Power BI), and strong business problem-solving abilities. You should be adept at data visualization, communicating technical concepts to non-technical audiences, and collaborating with cross-functional teams. Experience with large-scale data warehousing, real-time analytics, and understanding gaming or sports entertainment metrics are highly valued.

5.5 How long does the DraftKings Business Intelligence hiring process take?
The typical DraftKings Business Intelligence hiring process lasts 3–5 weeks from initial application to offer. Timelines can vary based on candidate availability, scheduling logistics, and the complexity of the take-home assignment. Fast-track candidates or those with internal referrals may move through the process in as little as 2–3 weeks.

5.6 What types of questions are asked in the DraftKings Business Intelligence interview?
Expect a mix of technical, case-based, and behavioral questions. Technical interviews focus on SQL, data modeling, ETL pipeline design, and dashboard development. Case studies may ask you to solve business problems using real or hypothetical DraftKings data, design reporting solutions, or interpret gaming metrics. Behavioral rounds assess collaboration, adaptability, stakeholder management, and communication skills. You’ll also be asked about handling ambiguity, prioritizing deadlines, and influencing stakeholders without formal authority.

5.7 Does DraftKings give feedback after the Business Intelligence interview?
DraftKings generally provides high-level feedback through the recruiter, especially if you progress to the later stages. While detailed technical feedback may be limited, you can expect some insights into your strengths and areas for improvement if you request it.

5.8 What is the acceptance rate for DraftKings Business Intelligence applicants?
DraftKings Business Intelligence roles are highly competitive, with an estimated acceptance rate of 3–6% for qualified applicants. The company seeks candidates with a strong blend of technical expertise, business acumen, and exceptional communication skills.

5.9 Does DraftKings hire remote Business Intelligence positions?
Yes, DraftKings offers remote positions for Business Intelligence roles, although some may require occasional travel for team collaboration or onsite meetings. Flexibility varies by team and role, so clarify expectations during your interview process.

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  • Business Intelligence interview guide
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  • How to Prepare for Business Intelligence Interviews: Success Story
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Ready to ace your DraftKings Business Intelligence interview? It’s not just about knowing the technical skills—you need to think like a DraftKings Business Intelligence professional, solve problems under pressure, and connect your expertise to real business impact. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at DraftKings and similar companies.

With resources like the DraftKings Business Intelligence Interview Guide and our latest case study practice sets, you’ll get access to real interview questions, detailed walkthroughs, and coaching support designed to boost both your technical skills and domain intuition. Dive into targeted preparation for data pipeline design, dashboard creation, stakeholder communication, and deriving actionable insights—skills that set top candidates apart in DraftKings’ fast-paced, data-driven environment.

Take the next step—explore more case study questions, try mock interviews, and browse targeted prep materials on Interview Query. Bookmark this guide or share it with peers prepping for similar roles. It could be the difference between applying and offering. You’ve got this!