Educational Testing Service (Ets) Business Intelligence Interview Guide

Getting ready for a Business Intelligence interview at Educational Testing Service (ETS)? The ETS Business Intelligence interview process typically spans a variety of question topics and evaluates skills in areas like data warehousing, ETL pipeline design, data analysis, dashboard creation, and stakeholder communication. Interview preparation is especially important for this role at ETS, as candidates are expected to demonstrate a strong ability to ensure data quality, present actionable insights, and translate complex analytics into accessible recommendations for diverse audiences within an education-focused organization.

In preparing for the interview, you should:

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

Educational Testing Service (ETS) is a leading nonprofit organization specializing in educational assessment, research, and testing services worldwide. ETS develops, administers, and scores standardized tests such as the GRE, TOEFL, and Praxis, serving millions of students, educators, and institutions annually. Committed to advancing quality and equity in education, ETS uses data-driven insights to inform policy and improve learning outcomes. In a Business Intelligence role, you will contribute to ETS’s mission by transforming complex data into actionable intelligence, supporting decision-making across educational programs and organizational operations.

As a Business Intelligence professional at Educational Testing Service (ETS), you are responsible for transforming data into actionable insights that support the organization’s mission of advancing quality and equity in education. You will collect, analyze, and interpret data from various sources to inform decision-making across departments such as product development, operations, and marketing. Core tasks include designing dashboards, generating reports, and identifying trends to optimize business processes and improve test administration. By collaborating with cross-functional teams, you help ETS enhance its assessment services and drive strategic initiatives to better serve educators, students, and institutions worldwide.

2.1 Stage 1: Application & Resume Review

The initial screening assesses your experience in business intelligence, data warehousing, ETL pipeline development, dashboard creation, and data visualization. The review focuses on your ability to manage complex data sources, ensure data quality, and communicate insights effectively to both technical and non-technical stakeholders. Applications are typically evaluated by the HR team in collaboration with BI managers.

2.2 Stage 2: Recruiter Screen

This stage involves a phone or video call with a recruiter, lasting 30–45 minutes. You’ll discuss your background, motivation for joining ETS, and high-level skills in analytics and business intelligence. The recruiter will clarify your experience with tools such as SQL, Python, and data visualization platforms, as well as your approach to stakeholder communication and cross-functional collaboration. To prepare, be ready to concisely articulate your relevant experience and interest in educational data challenges.

2.3 Stage 3: Technical/Case/Skills Round

Conducted by BI team leads or analytics managers, this round tests your technical proficiency through a mix of case studies, system design scenarios, and hands-on exercises. Expect to demonstrate skills in designing scalable ETL pipelines, architecting data warehouses, constructing dashboards, and analyzing diverse datasets for actionable insights. You may be asked to solve SQL problems, describe approaches to data cleaning and integration, or outline strategies for addressing data quality issues and experiment validity. Preparation should include reviewing your experience with business intelligence tools, statistical analysis, and real-world data project challenges.

2.4 Stage 4: Behavioral Interview

Led by BI managers or cross-functional partners, this interview evaluates your communication style, adaptability, and stakeholder management abilities. You’ll be asked to share examples of translating complex analytics for non-technical audiences, resolving project hurdles, and aligning expectations with business units. Focus on providing clear, structured responses that highlight your leadership and teamwork in BI environments. Practice discussing how you make data accessible and actionable for a variety of audiences.

2.5 Stage 5: Final/Onsite Round

The final round typically consists of a series of interviews with senior BI leaders, analytics directors, and sometimes business stakeholders. Sessions may include deep dives into previous data projects, live technical problem-solving, and presentations of complex insights tailored for executive audiences. You may also interact with cross-functional teams to assess your fit within ETS’s collaborative culture. Preparation should emphasize your ability to design and communicate end-to-end BI solutions and drive data-driven decision making at scale.

2.6 Stage 6: Offer & Negotiation

After successful completion of all rounds, the recruiter will reach out to discuss the offer package, compensation, start date, and any remaining questions. This stage may also include a final conversation with the hiring manager to ensure alignment on role expectations and growth opportunities.

2.7 Average Timeline

The ETS Business Intelligence interview process typically spans 3–5 weeks from application to offer. Candidates with highly relevant experience and strong technical skills may move through the process more quickly, with fast-track timelines of 2–3 weeks. Standard pacing allows about a week between each stage, with technical and onsite rounds scheduled based on team availability and candidate flexibility.

Next, let’s break down the types of interview questions you can expect throughout the ETS Business Intelligence interview process.

In interviews for Business Intelligence roles at ETS, you can expect a blend of technical, analytical, and communication-focused questions. The process typically evaluates your ability to design robust data solutions, ensure data quality, synthesize insights, and present findings to diverse audiences. Below is a curated selection of questions grouped by topic to help you prepare for the range of scenarios you might encounter.

3.1 Data Engineering & ETL

This category assesses your ability to design, implement, and troubleshoot data pipelines and warehouses. Expect questions on ETL best practices, data integration, and handling large-scale data systems.

3.1.1 Ensuring data quality within a complex ETL setup
Explain how you would identify and resolve data quality issues in a multi-source ETL pipeline. Highlight your use of validation rules, monitoring, and reconciliation techniques.

3.1.2 Let's say that you're in charge of getting payment data into your internal data warehouse.
Discuss your approach to designing a reliable payment data pipeline, including data ingestion, validation, error handling, and monitoring for integrity.

3.1.3 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Describe the architecture and technologies you would use for scalability, data normalization, and fault tolerance when integrating diverse external data sources.

3.1.4 Design a data warehouse for a new online retailer
Outline your process for determining schema, data modeling, and partitioning strategies to support analytics and reporting for a retail business.

3.1.5 How would you design a data warehouse for a e-commerce company looking to expand internationally?
Explain considerations for handling localization, regulatory requirements, and multi-region performance in a global data warehouse setup.

3.2 Data Analysis & Experimentation

These questions focus on your proficiency with statistical testing, experiment design, and deriving actionable insights from complex datasets.

3.2.1 An A/B test is being conducted to determine which version of a payment processing page leads to higher conversion rates. You’re responsible for analyzing the results. How would you set up and analyze this A/B test? Additionally, how would you use bootstrap sampling to calculate the confidence intervals for the test results, ensuring your conclusions are statistically valid?
Walk through your process for experiment setup, metric definition, statistical analysis, and communicating uncertainty using bootstrap methods.

3.2.2 The role of A/B testing in measuring the success rate of an analytics experiment
Describe how you would design and interpret an A/B test, including defining success criteria and ensuring experiment validity.

3.2.3 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 process for data cleaning, integration, and analysis, focusing on strategies to handle discrepancies and extract actionable recommendations.

3.2.4 How would you evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Discuss how you would design an experiment or analysis, select key metrics, and measure short- and long-term business impact.

3.3 SQL & Data Manipulation

Expect questions that test your ability to write efficient queries, handle large datasets, and troubleshoot data integrity issues.

3.3.1 Write a SQL query to count transactions filtered by several criterias.
Demonstrate your approach to filtering, aggregating, and optimizing queries for performance.

3.3.2 Write a query to get the current salary for each employee after an ETL error.
Show how you would identify and correct errors in data, ensuring accurate and up-to-date reporting.

3.3.3 Modifying a billion rows
Explain strategies for updating massive tables efficiently, considering locking, batching, and minimizing downtime.

3.3.4 List out the exams sources of each student in MySQL
Describe your method for joining and aggregating data to produce comprehensive reports.

3.4 Data Visualization & Communication

This group evaluates your ability to translate technical analyses into clear, actionable insights for varied audiences.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Share your approach to tailoring presentations, simplifying concepts, and using visualizations to engage stakeholders.

3.4.2 Making data-driven insights actionable for those without technical expertise
Discuss techniques for bridging the gap between technical findings and business decision-makers.

3.4.3 Demystifying data for non-technical users through visualization and clear communication
Describe how you design dashboards or reports that are intuitive and support self-service analytics.

3.4.4 Strategically resolving misaligned expectations with stakeholders for a successful project outcome
Explain your process for identifying misalignments early and facilitating consensus through data storytelling.

3.5 System & Dashboard Design

Questions here test your ability to architect analytics solutions and dashboards that scale and deliver value.

3.5.1 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.
Outline your process for requirements gathering, feature prioritization, and ensuring the dashboard drives business decisions.

3.5.2 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Walk through your approach for building scalable, reliable data pipelines that support predictive analytics.

3.5.3 System design for a digital classroom service.
Discuss the key components, data flows, and considerations for reliability and analytics in an educational context.

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3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Describe how you identified a business problem, analyzed relevant data, and communicated your data-driven recommendation, including the impact on the organization.

3.6.2 How do you handle unclear requirements or ambiguity?
Share a specific example where you sought clarification, iterated on initial assumptions, and ensured alignment with stakeholders before proceeding.

3.6.3 Describe a challenging data project and how you handled it.
Explain the obstacles you faced, your approach to problem-solving, and how you adapted your strategy to achieve a successful outcome.

3.6.4 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Discuss how you adapted your communication style, used visuals or prototypes, and ensured mutual understanding.

3.6.5 Walk us through how you handled conflicting KPI definitions (e.g., “active user”) between two teams and arrived at a single source of truth.
Detail your process for facilitating discussions, aligning on definitions, and implementing documentation to prevent future confusion.

3.6.6 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Describe the strategies you used to build credibility, present evidence, and gain buy-in.

3.6.7 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Explain the tools or scripts you built, how you integrated them into workflows, and the resulting improvements in efficiency and reliability.

3.6.8 Describe a time you had to deliver an overnight churn report and still guarantee the numbers were “executive reliable.” How did you balance speed with data accuracy?
Discuss your triage process, prioritization of critical checks, and communication of any limitations or uncertainties.

3.6.9 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Highlight how early visualization or prototyping helped surface misalignments and drive consensus.

3.6.10 Tell us about a project where you had to make a tradeoff between speed and accuracy.
Describe the context, how you evaluated the risks, and how you communicated trade-offs to stakeholders.

4.1 Company-specific tips:

Get familiar with ETS’s mission to advance quality and equity in education, and understand how data-driven insights support their strategic goals. Review ETS’s core products—such as GRE, TOEFL, and Praxis—and consider how business intelligence can optimize test administration, improve student outcomes, and inform educational policy decisions. Be ready to discuss how BI can help ETS deliver fair, reliable, and actionable information to educators, institutions, and policy makers.

Research recent ETS initiatives in digital assessment, remote testing, and educational research. Think about the kinds of data challenges these initiatives present, such as ensuring data privacy, integrating new data sources, and supporting diverse stakeholders. Prepare to show your awareness of the unique regulatory and ethical considerations in educational data, and how you would address them in your BI work.

Demonstrate your ability to communicate complex analytics in a clear, accessible way to non-technical audiences. ETS values professionals who can bridge the gap between technical analysis and actionable recommendations for educators and administrators. Practice explaining technical BI concepts with real-world education examples, such as improving test reliability or identifying achievement gaps.

4.2 Role-specific tips:

4.2.1 Review best practices for designing scalable ETL pipelines and data warehouses.
Be prepared to discuss your approach to building robust ETL systems that can ingest, clean, and integrate data from multiple sources—such as test scores, student demographics, and operational logs. Highlight your experience with data validation, error handling, and monitoring to ensure data quality and reliability. Practice outlining how you would architect a data warehouse to support analytics and reporting for educational programs.

4.2.2 Practice analyzing and synthesizing insights from heterogeneous datasets.
Showcase your ability to clean, combine, and analyze data from diverse sources—such as payment transactions, user behavior, and fraud detection logs. Be ready to walk through your process for resolving discrepancies, handling missing data, and extracting actionable recommendations. Emphasize how you would use these insights to improve ETS’s services or inform strategic decisions.

4.2.3 Demonstrate proficiency in SQL for complex data manipulation and reporting.
Expect to write queries that filter, aggregate, and join large datasets, ensuring accuracy and performance. Practice troubleshooting data integrity issues, such as correcting errors after an ETL failure or updating massive tables efficiently. Prepare to discuss how you optimize queries for speed and reliability, and how you ensure reporting remains accurate and up-to-date.

4.2.4 Prepare examples of designing dashboards and visualizations for diverse stakeholders.
ETS values BI professionals who can create intuitive dashboards that support self-service analytics for educators, administrators, and executives. Be ready to describe your process for requirements gathering, feature prioritization, and tailoring visualizations to different audiences. Practice presenting complex data insights with clarity, using storytelling and visual design to drive engagement and decision-making.

4.2.5 Brush up on statistical analysis and experiment design, especially A/B testing.
Review how to set up, analyze, and interpret A/B tests, including defining success metrics, ensuring experiment validity, and calculating confidence intervals. Be prepared to communicate uncertainty and statistical significance in ways that are meaningful for non-technical stakeholders. Show how you would use experimentation to measure the impact of educational interventions or operational changes.

4.2.6 Practice communicating and resolving stakeholder misalignments.
You’ll need to align expectations and definitions—such as KPI definitions or project goals—across teams with different perspectives. Prepare stories that demonstrate your ability to facilitate consensus, clarify requirements, and use data prototypes or wireframes to surface misalignments early. Emphasize your adaptability and commitment to delivering solutions that meet business needs while maintaining data integrity.

4.2.7 Highlight your experience automating data-quality checks and ensuring reliability.
Be ready to discuss tools, scripts, or workflows you’ve built to automate recurrent data-quality checks, prevent dirty-data crises, and improve efficiency. Show how your approach has led to more reliable reporting and greater trust in BI outputs, especially when delivering time-sensitive reports to executive audiences.

4.2.8 Showcase your ability to balance speed and accuracy under pressure.
Share examples of delivering rapid analyses or overnight reports while maintaining “executive reliable” accuracy. Describe your triage process, prioritization of critical checks, and strategies for communicating limitations or uncertainties to stakeholders. This will demonstrate your judgment and professionalism in high-stakes BI environments.

5.1 How hard is the Educational Testing Service (ETS) Business Intelligence interview?
The ETS Business Intelligence interview is moderately to highly challenging, as it assesses both technical depth and communication skills. Candidates are expected to demonstrate expertise in data warehousing, ETL pipeline design, SQL, dashboard creation, and the ability to translate analytics for non-technical stakeholders. The educational context adds complexity, requiring awareness of data privacy, regulatory issues, and the impact of data-driven insights on educational outcomes.

5.2 How many interview rounds does Educational Testing Service (ETS) have for Business Intelligence?
Typically, the ETS Business Intelligence interview process consists of 5–6 rounds: an initial resume review, recruiter screen, technical/case/skills interview, behavioral interview, final onsite (or virtual) round with senior BI leaders, and the offer/negotiation stage. Each round is designed to evaluate a different aspect of your skills and fit for the organization.

5.3 Does Educational Testing Service (ETS) ask for take-home assignments for Business Intelligence?
ETS occasionally includes take-home assignments or case studies, especially for senior or specialized BI roles. These assignments may involve designing an ETL pipeline, building a dashboard, or analyzing a dataset to extract actionable insights relevant to educational programs. The goal is to assess your practical problem-solving abilities and communication skills.

5.4 What skills are required for the Educational Testing Service (ETS) Business Intelligence?
Key skills include advanced SQL, data warehousing, ETL pipeline design, data visualization, dashboard creation, statistical analysis, and experiment design (such as A/B testing). Strong stakeholder communication and the ability to present complex insights in accessible ways are essential. Familiarity with educational data, regulatory compliance, and experience in cross-functional collaboration are highly valued.

5.5 How long does the Educational Testing Service (ETS) Business Intelligence hiring process take?
The typical timeline is 3–5 weeks from application to offer. Fast-track candidates may complete the process in as little as 2–3 weeks, while standard pacing allows about a week between each stage. Scheduling depends on team availability and candidate flexibility.

5.6 What types of questions are asked in the Educational Testing Service (ETS) Business Intelligence interview?
Expect a mix of technical, analytical, and behavioral questions. Technical questions cover ETL pipeline design, data warehousing, SQL, dashboard development, and statistical analysis. Analytical scenarios may involve experiment design, data integration, and interpreting educational data. Behavioral questions focus on stakeholder communication, resolving ambiguity, and aligning cross-functional teams.

5.7 Does Educational Testing Service (ETS) give feedback after the Business Intelligence interview?
ETS typically provides high-level feedback through recruiters, especially if you progress to later rounds. Detailed technical feedback may be limited, but you can expect insights into your strengths and areas for improvement based on interview performance.

5.8 What is the acceptance rate for Educational Testing Service (ETS) Business Intelligence applicants?
While specific rates are not publicly available, the Business Intelligence role at ETS is competitive. Estimated acceptance rates are in the range of 3–6% for qualified applicants, reflecting high standards for both technical and communication skills.

5.9 Does Educational Testing Service (ETS) hire remote Business Intelligence positions?
Yes, ETS offers remote opportunities for Business Intelligence professionals, with some roles requiring occasional office visits or travel for team collaboration. The organization supports flexible work arrangements, especially for candidates with strong technical and communication skills.

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Ready to ace your Educational Testing Service (ETS) Business Intelligence interview? It’s not just about knowing the technical skills—you need to think like an ETS 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 ETS and similar companies.

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