Chartboost Business Intelligence Interview Guide

Getting ready for a Business Intelligence interview at Chartboost? The Chartboost Business Intelligence interview process typically spans 4–6 question topics and evaluates skills in areas like data analytics, dashboard design, data visualization, SQL, and business strategy. Because Chartboost operates in the dynamic mobile advertising and monetization space, interview prep is essential—candidates must demonstrate the ability to translate complex data into actionable insights, design effective reporting solutions, and communicate findings clearly to both technical and non-technical stakeholders.

In preparing for the interview, you should:

• Understand the core skills necessary for Business Intelligence positions at Chartboost.
• Gain insights into Chartboost’s Business Intelligence interview structure and process.
• Practice real Chartboost 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 Chartboost Business Intelligence interview process, along with sample questions and preparation tips tailored to help you succeed.

Chartboost is a leading mobile ad platform that connects app developers with advertisers to drive user acquisition and monetization. Operating at scale, Chartboost supports thousands of mobile apps worldwide, providing tools for in-app advertising, mediation, and analytics. The company leverages data-driven insights to optimize ad performance and maximize revenue for its partners. As a Business Intelligence professional, you will play a crucial role in analyzing data trends, generating actionable insights, and supporting Chartboost’s mission to empower developers and advertisers in the mobile ecosystem.

As a Business Intelligence professional at Chartboost, you are responsible for transforming raw data into actionable insights that inform strategic decision-making across the organization. You will work closely with product, marketing, and operations teams to analyze user behavior, advertising performance, and market trends within the mobile gaming and ad tech space. Key tasks include designing dashboards, generating reports, and presenting data-driven recommendations to stakeholders. This role is essential for optimizing revenue streams, improving product offerings, and supporting Chartboost’s mission to empower mobile app developers and advertisers with effective monetization solutions.

2.1 Stage 1: Application & Resume Review

The process begins with a thorough review of your application materials, focusing on your experience in business intelligence, data analytics, dashboard design, and your proficiency with SQL and Python. The hiring team is looking for evidence of data-driven decision-making, experience with ETL processes, and the ability to communicate insights clearly to both technical and non-technical stakeholders. Tailor your resume to highlight projects involving data pipelines, dashboarding, and cross-functional collaboration.

2.2 Stage 2: Recruiter Screen

Next, you’ll have a conversation with a Chartboost recruiter, typically lasting 30 minutes. This screen assesses your motivation for joining Chartboost, your understanding of the business intelligence function, and your general fit for the company culture. Expect to discuss your background, interest in mobile advertising analytics, and experience with presenting data insights. Prepare by researching Chartboost’s business model and reflecting on how your skills align with their mission.

2.3 Stage 3: Technical/Case/Skills Round

This round delves into your technical expertise and problem-solving abilities. You may be asked to solve SQL queries, design dashboards for business scenarios, or outline approaches for data warehousing and ETL pipelines. The interviewers will probe your ability to analyze complex datasets, synthesize insights for decision-makers, and address challenges in data projects. Preparation should include refreshing your knowledge of SQL, Python, data modeling, and best practices for data visualization and pipeline design.

2.4 Stage 4: Behavioral Interview

The behavioral interview evaluates your soft skills, collaboration style, and adaptability. Expect questions about overcoming hurdles in data projects, communicating findings to non-technical users, and handling ambiguous requirements. The interviewers are interested in how you approach stakeholder engagement, tailor presentations for different audiences, and drive actionable recommendations from analytics work. Prepare examples that showcase your teamwork, leadership, and ability to demystify complex data.

2.5 Stage 5: Final/Onsite Round

The final stage typically consists of several interviews with business intelligence team members, senior data analysts, and product managers. You may be asked to present a case study, walk through a dashboard you’ve built, or interpret trends in real-world datasets. This round assesses your depth of technical knowledge, strategic thinking, and ability to collaborate cross-functionally. Preparation should include practicing data storytelling, dashboard walkthroughs, and discussing past projects with a focus on impact and business outcomes.

2.6 Stage 6: Offer & Negotiation

If successful, you will receive an offer from Chartboost’s recruiting team. This stage involves discussing compensation, benefits, and expectations around your role. Be ready to negotiate based on your experience and the value you bring in business intelligence, analytics, and data-driven strategy.

2.7 Average Timeline

The Chartboost Business Intelligence interview process typically spans 3-5 weeks from initial application to final offer. Fast-track candidates with highly relevant experience may complete the process in as little as 2-3 weeks, while the standard pace allows for a week or more between each stage. Onsite rounds are usually scheduled within a few days of technical and behavioral interviews, and the offer stage moves quickly once a decision is made.

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

3.1 Data Analytics & Experimentation

These questions focus on your ability to design experiments, measure business outcomes, and extract actionable insights from complex datasets. Expect to discuss A/B testing, campaign success measurement, and how to evaluate the impact of business decisions using data.

3.1.1 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?
Outline an experimental design (A/B test or quasi-experiment), discuss key metrics (retention, conversion, profitability), and consider confounding factors. Reference how you’d monitor short-term and long-term effects.

3.1.2 How would you measure the success of an email campaign?
Identify relevant KPIs (open rate, CTR, conversion), discuss segmentation, and describe how you’d set up tracking and analyze lift versus baseline. Mention how you’d handle attribution and avoid misleading conclusions.

3.1.3 The role of A/B testing in measuring the success rate of an analytics experiment
Describe the principles of randomization, control groups, and statistical significance. Illustrate how you’d interpret results and communicate actionable recommendations.

3.1.4 *We're interested in how user activity affects user purchasing behavior. *
Explain how you’d use cohort analysis, regression, or propensity modeling to link engagement metrics to purchase outcomes. Emphasize the importance of controlling for confounders and time windows.

3.1.5 How would you analyze the dataset to understand exactly where the revenue loss is occurring?
Discuss segmenting by product, channel, or cohort, running time-series analysis, and investigating anomalies. Highlight the importance of root cause analysis and clear communication of findings.

3.2 Dashboarding & Visualization

Expect questions about designing dashboards, choosing metrics, and communicating insights to both technical and non-technical audiences. The focus is on making complex data accessible and actionable for stakeholders.

3.2.1 Which metrics and visualizations would you prioritize for a CEO-facing dashboard during a major rider acquisition campaign?
Explain how you’d select high-level KPIs, use summary visuals, and provide drill-downs for deeper analysis. Stress the importance of clarity and relevance for executive decision-making.

3.2.2 Design a dashboard that provides personalized insights, sales forecasts, and inventory recommendations for shop owners based on their transaction history, seasonal trends, and customer behavior.
Describe how you’d leverage segmentation, predictive modeling, and intuitive visualizations. Discuss balancing customization with scalability and usability.

3.2.3 Designing a dynamic sales dashboard to track McDonald's branch performance in real-time
Discuss real-time data integration, alerting, and key metrics selection. Emphasize how you’d ensure performance, reliability, and actionable insights.

3.2.4 How to present complex data insights with clarity and adaptability tailored to a specific audience
Focus on storytelling, audience adaptation, and choosing the right visualizations. Mention techniques for simplifying technical findings for business leaders.

3.2.5 Demystifying data for non-technical users through visualization and clear communication
Explain how you’d use intuitive charts, plain language, and interactive elements. Highlight the importance of empathy for the audience’s perspective.

3.3 Data Engineering & Pipeline Design

These questions assess your ability to build scalable data systems, design schemas, and ensure data quality across multiple sources. Prepare to discuss ETL, pipeline reliability, and handling large datasets.

3.3.1 Design a data warehouse for a new online retailer
Outline schema design, dimensional modeling, and ETL strategies. Discuss balancing flexibility, scalability, and query performance.

3.3.2 Design a data pipeline for hourly user analytics.
Describe ingestion, aggregation, and storage layers. Emphasize monitoring, fault tolerance, and how you’d optimize for speed and accuracy.

3.3.3 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Discuss data sources, preprocessing, feature engineering, and model deployment. Highlight reliability and scalability considerations.

3.3.4 You’re tasked with analyzing data from multiple sources, such as payment transactions, user behavior, and fraud detection logs. How would you approach solving a data analytics problem involving these diverse datasets? What steps would you take to clean, combine, and extract meaningful insights that could improve the system's performance?
Explain your approach to data cleaning, schema reconciliation, and joining disparate sources. Highlight best practices for ensuring consistency and extracting actionable insights.

3.3.5 Ensuring data quality within a complex ETL setup
Discuss data validation, monitoring, and automated checks. Share your experience with resolving data inconsistencies and maintaining trust in analytics outputs.

3.4 SQL & Data Manipulation

Expect questions assessing your ability to write efficient SQL queries, manipulate large datasets, and derive business insights directly from raw data.

3.4.1 Write a SQL query to count transactions filtered by several criterias.
Demonstrate your ability to filter, aggregate, and optimize queries for performance. Discuss handling edge cases and ensuring correctness.

3.4.2 Calculate total and average expenses for each department.
Show how to group by department, use aggregate functions, and format results for clarity. Mention strategies for handling missing or outlier data.

3.4.3 Modifying a billion rows
Describe best practices for bulk updates, transaction management, and minimizing downtime. Discuss partitioning, indexing, and rollback strategies.

3.4.4 python-vs-sql
Explain when you’d use SQL for data manipulation versus Python for advanced analytics. Illustrate with examples from past projects.

3.4.5 Design a database for a ride-sharing app.
Outline key entities, relationships, and normalization strategies. Emphasize scalability and support for analytics queries.

3.5 Behavioral Questions

3.5.1 Tell Me About a Time You Used Data to Make a Decision
Describe a situation where your analysis led to a measurable business impact. Focus on the problem, your approach, and the outcome.

3.5.2 Describe a Challenging Data Project and How You Handled It
Share details about a complex project, the obstacles you encountered, and how you overcame them. Highlight problem-solving and adaptability.

3.5.3 How Do You Handle Unclear Requirements or Ambiguity?
Explain your process for clarifying goals, communicating with stakeholders, and iterating on solutions. Emphasize proactive questioning and flexibility.

3.5.4 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Reflect on a situation where miscommunication occurred, your steps to resolve it, and what you learned about stakeholder engagement.

3.5.5 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Discuss how you prioritized requests, communicated trade-offs, and maintained project focus. Mention frameworks or techniques used.

3.5.6 When leadership demanded a quicker deadline than you felt was realistic, what steps did you take to reset expectations while still showing progress?
Share how you communicated risks, re-scoped deliverables, and provided interim results to maintain transparency and trust.

3.5.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation
Describe your approach to building consensus, presenting evidence, and persuading teams to act on your insights.

3.5.8 Describe how you prioritized backlog items when multiple executives marked their requests as “high priority.”
Explain your prioritization framework, communication strategy, and how you ensured alignment with business objectives.

3.5.9 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, the methods you used, and how you communicated limitations and confidence to stakeholders.

3.5.10 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable
Highlight your use of visualization, iterative feedback, and collaborative design to achieve consensus and clarity.

4.1 Company-specific tips:

Immerse yourself in the mobile advertising ecosystem to understand Chartboost’s unique position as a platform connecting app developers and advertisers. Research how Chartboost leverages data-driven insights to optimize ad performance, drive user acquisition, and maximize revenue for mobile apps. Familiarize yourself with the company’s core products—such as mediation, analytics, and in-app advertising—and recent industry trends in mobile monetization. Knowing the business context will help you frame your answers and show genuine interest in Chartboost’s mission.

Review Chartboost’s approach to empowering developers and advertisers. Investigate how the company uses data to inform product decisions, support partners, and differentiate itself from competitors. Be ready to discuss how business intelligence can directly impact product innovation and revenue optimization within the mobile gaming and ad tech space.

Understand the key stakeholders you’ll collaborate with as a Business Intelligence professional at Chartboost. These include product managers, marketing teams, and operations. Prepare to articulate how you would tailor your insights and dashboards to support the unique goals and challenges of each group, demonstrating your ability to drive cross-functional impact.

4.2 Role-specific tips:

4.2.1 Practice designing dashboards that highlight mobile ad performance, user engagement, and revenue trends.
Create sample dashboards that showcase essential metrics for Chartboost’s business model, such as fill rate, eCPM, retention, and conversion rates. Focus on clarity, relevance, and the ability to provide actionable insights for both executive and operational audiences.

4.2.2 Prepare to discuss experimental design and A/B testing in the context of mobile advertising.
Be ready to outline how you would evaluate the impact of new ad formats, promotional campaigns, or product features using rigorous experimentation. Emphasize your understanding of randomization, control groups, and the interpretation of statistical significance for business decisions.

4.2.3 Strengthen your SQL skills with queries involving time-series analysis, cohort segmentation, and large-scale data manipulation.
Practice writing efficient SQL queries to analyze user behavior, ad performance, and revenue by various dimensions—such as geography, device type, or campaign. Demonstrate your ability to handle big data, optimize query performance, and ensure accuracy in reporting.

4.2.4 Be prepared to design and discuss ETL pipelines for aggregating and cleaning diverse data sources.
Articulate your approach to building robust data pipelines that ingest data from ad networks, app events, and third-party analytics. Highlight techniques for ensuring data quality, reliability, and scalability, as well as your experience with schema design and data validation.

4.2.5 Demonstrate your ability to communicate complex data insights to non-technical stakeholders.
Prepare examples of how you have simplified technical findings for business leaders, using storytelling, intuitive visualizations, and plain language. Show empathy for your audience and adaptability in your communication style.

4.2.6 Reflect on past experiences where you translated messy, incomplete, or ambiguous data into actionable recommendations.
Share stories of handling missing values, resolving inconsistencies, and making analytical trade-offs. Emphasize your problem-solving skills and your ability to communicate limitations and confidence levels to stakeholders.

4.2.7 Prepare to answer behavioral questions with examples that showcase stakeholder engagement, project management, and data-driven decision-making.
Think about times when you influenced others without formal authority, negotiated scope, or prioritized competing requests. Use these stories to highlight your leadership, collaboration, and strategic thinking skills.

4.2.8 Review your experience with cross-functional projects and the impact of your insights on business outcomes.
Be ready to discuss how your analyses led to measurable improvements in product performance, user acquisition, or revenue optimization. Focus on the business impact and your role in driving change.

4.2.9 Practice presenting dashboards, case studies, or data prototypes to diverse audiences.
Prepare to walk interviewers through your design choices, explain the rationale behind your metrics and visualizations, and respond to feedback or follow-up questions. This will demonstrate your ability to align stakeholders and deliver value through business intelligence.

4.2.10 Brush up on your knowledge of the mobile ad tech space, including key metrics, common challenges, and emerging trends.
Stay informed about industry benchmarks, privacy regulations, and new technologies affecting mobile advertising. This will help you contextualize your answers and show that you are ready to contribute to Chartboost’s evolving business environment.

5.1 “How hard is the Chartboost Business Intelligence interview?”
The Chartboost Business Intelligence interview is considered moderately challenging, especially for those without prior experience in mobile advertising or ad tech analytics. The process tests your ability to analyze complex datasets, design effective dashboards, and communicate insights clearly. Expect to be evaluated on both technical depth (SQL, data modeling, pipeline design) and business acumen (translating data into actionable recommendations for product and marketing teams). Candidates who prepare for scenario-based questions and demonstrate a strong understanding of the mobile monetization landscape tend to perform best.

5.2 “How many interview rounds does Chartboost have for Business Intelligence?”
Chartboost typically has 4-6 interview rounds for Business Intelligence roles. The process starts with an application and resume review, followed by a recruiter screen. Next are technical and case/skills interviews, a behavioral round, and a final onsite or virtual panel interview with team members and cross-functional stakeholders. Each stage assesses a different set of competencies, from analytical skills and technical expertise to communication and cultural fit.

5.3 “Does Chartboost ask for take-home assignments for Business Intelligence?”
Take-home assignments are sometimes included, especially for candidates progressing to the later rounds. These assignments may involve analyzing a dataset, designing a dashboard, or solving a business case relevant to mobile advertising. The goal is to assess your practical skills—how you approach real-world data problems, structure your analysis, and present your findings in a clear, actionable manner.

5.4 “What skills are required for the Chartboost Business Intelligence?”
Essential skills include advanced SQL and data manipulation, dashboard and data visualization design, and experience with data pipeline development (ETL). Strong analytical thinking, business strategy understanding, and the ability to translate data into insights for non-technical stakeholders are crucial. Familiarity with Python or R, mobile ad tech metrics (e.g., eCPM, fill rate, retention), and experience working with large, messy datasets are highly valued. Communication and stakeholder management skills are also key to success.

5.5 “How long does the Chartboost Business Intelligence hiring process take?”
The hiring process typically takes 3-5 weeks from application to offer. Some candidates may move faster—within 2-3 weeks—if interview scheduling and feedback cycles are expedited. Each interview stage is usually spaced a few days to a week apart, with the final offer and negotiation happening quickly after successful onsite or panel interviews.

5.6 “What types of questions are asked in the Chartboost Business Intelligence interview?”
You will encounter a mix of technical, analytical, and behavioral questions. Technical questions focus on SQL, data pipelines, ETL, and dashboard design. Analytical questions assess your ability to conduct cohort analysis, A/B testing, and root cause investigations in the context of mobile advertising. Behavioral questions explore your experience working cross-functionally, communicating with non-technical stakeholders, and driving business impact through data. Expect scenario-based and case study questions relevant to ad tech and mobile monetization.

5.7 “Does Chartboost give feedback after the Business Intelligence interview?”
Chartboost generally provides feedback through the recruiting team, especially if you reach the later stages of the process. While detailed technical feedback may be limited, you can expect high-level insights about your performance and areas for improvement. The company values candidate experience and strives to keep communication transparent throughout the process.

5.8 “What is the acceptance rate for Chartboost Business Intelligence applicants?”
The acceptance rate for Chartboost Business Intelligence roles is competitive, reflecting the company’s high standards and the specialized skills required. While exact figures are not public, it is estimated that only about 3-6% of applicants receive offers. Candidates with a strong background in mobile analytics, business intelligence, and stakeholder communication have a higher chance of success.

5.9 “Does Chartboost hire remote Business Intelligence positions?”
Yes, Chartboost offers remote opportunities for Business Intelligence roles, especially for candidates with strong technical skills and proven ability to collaborate virtually. Some positions may require occasional visits to the office for team meetings or project kickoffs, but remote work is supported, reflecting Chartboost’s flexible and modern work environment.

• Related guides:
  • Chartboost interview questions
  • Business Intelligence interview guide
  • Top Business Intelligence interview tips

Ready to ace your Chartboost Business Intelligence interview? It’s not just about knowing the technical skills—you need to think like a Chartboost 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 Chartboost and similar companies.

With resources like the Chartboost 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. Whether you’re tackling SQL challenges, designing dashboards for mobile ad performance, or translating complex data into actionable recommendations, these tools will help you showcase the analytical rigor and business acumen Chartboost is looking for.

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!