Great american insurance group Business Intelligence Interview Guide
1. Introduction
Getting ready for a Business Intelligence interview at Great American Insurance Group? The Great American Insurance Group Business Intelligence interview process typically spans several question topics and evaluates skills in areas like data analysis, dashboard design, stakeholder communication, and data warehousing. Interview preparation is especially important for this role, as candidates are expected to demonstrate their ability to transform complex data into actionable business insights, design scalable reporting solutions, and communicate findings effectively to both technical and non-technical audiences.
In preparing for the interview, you should:
- Understand the core skills necessary for Business Intelligence positions at Great American Insurance Group.
- Gain insights into Great American Insurance Group’s Business Intelligence interview structure and process.
- Practice real Great American Insurance Group 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 Great American Insurance Group Business Intelligence interview process, along with sample questions and preparation tips tailored to help you succeed.
1.2. What Great American Insurance Group Does
Great American Insurance Group is a leading provider of property and casualty insurance solutions, specializing in niche markets and tailored coverage for businesses and individuals. With a history dating back over 150 years, the company operates across North America and is known for its financial strength, customer-focused approach, and commitment to integrity. As a Business Intelligence professional, you will contribute to data-driven decision-making, helping the company optimize operations and deliver exceptional value to clients in a competitive insurance landscape.
1.3. What does a Great American Insurance Group Business Intelligence professional do?
As a Business Intelligence professional at Great American Insurance Group, you are responsible for transforming raw data into actionable insights that support strategic decision-making across the organization. You will work closely with business units to gather requirements, develop and maintain dashboards and reports, and analyze trends in insurance operations, claims, and customer behavior. Your role involves leveraging data visualization tools and advanced analytics to identify opportunities for process improvements and drive business growth. By providing accurate and timely information, you help the company optimize performance and deliver value to clients and stakeholders.
2. Overview of the Great American Insurance Group Interview Process
2.1 Stage 1: Application & Resume Review
During this initial stage, your application and resume are carefully evaluated by the HR team and relevant hiring managers within the Business Intelligence department. They look for experience in data analytics, business intelligence tool proficiency (such as Tableau, Power BI, or similar), SQL expertise, and a history of delivering actionable insights from complex datasets. Emphasis is placed on your ability to communicate data-driven findings and your familiarity with insurance or financial data environments. To prepare, ensure your resume highlights quantifiable achievements, technical skills, and any experience with data visualization, ETL processes, or reporting systems.
2.2 Stage 2: Recruiter Screen
The recruiter screen is typically a 20-30 minute phone interview led by an HR representative. This conversation focuses on your background, motivation for applying, and alignment with the Business Intelligence team’s culture and mission. Expect to discuss your professional trajectory, interest in insurance analytics, and high-level expectations for the role. Preparation should include a concise summary of your experience, a clear rationale for why you want to join Great American Insurance Group, and an understanding of how your skills meet the needs of a BI function.
2.3 Stage 3: Technical/Case/Skills Round
This stage may be conducted in person or virtually with members of the Business Intelligence & Analytics (BIA) department. The format is often a roundtable or panel interview involving multiple stakeholders such as BI analysts, data engineers, and potential cross-functional partners. You are assessed on your technical capabilities—such as writing SQL queries to analyze transactions, designing scalable ETL pipelines, or conceptualizing data warehouse solutions for insurance or financial data. Additionally, you may be asked to walk through business analytics case studies, discuss metrics for evaluating business promotions, or explain how you would visualize and present complex insights to non-technical audiences. Prepare by reviewing common business intelligence scenarios, practicing clear communication of technical solutions, and brushing up on practical SQL and dashboarding skills.
2.4 Stage 4: Behavioral Interview
Behavioral interviews are often integrated into the roundtable or conducted as a dedicated session with BI team members and leadership. The focus is on evaluating your problem-solving approach, teamwork and collaboration, adaptability, and ability to communicate with stakeholders from technical and non-technical backgrounds. You may be asked to describe challenges faced in past data projects, how you ensured data quality, and ways you made insights accessible to business users. Preparation should include reflecting on specific examples that demonstrate your leadership in BI initiatives, stakeholder management, and adaptability in ambiguous or fast-changing environments.
2.5 Stage 5: Final/Onsite Round
The final stage typically involves an in-person onsite interview, which may be a continuation of the roundtable format or a series of one-on-one meetings with senior BI team members, department managers, and HR. This round is designed to assess your cultural fit, communication skills, and ability to collaborate across functions. You may also be asked to present a data-driven project or walk through a portfolio of analytics work, highlighting how your insights influenced business outcomes. Prepare by selecting projects that showcase your end-to-end BI process, from data extraction and cleaning to visualization and executive-level communication.
2.6 Stage 6: Offer & Negotiation
After successful completion of the previous rounds, HR will reach out—often promptly—to discuss compensation, benefits, start date, and any role-specific details. This is your opportunity to clarify expectations, negotiate terms, and ensure alignment on your responsibilities within the Business Intelligence team.
2.7 Average Timeline
The average interview process for a Business Intelligence role at Great American Insurance Group spans approximately 2 to 4 weeks from application to offer. Candidates with highly relevant experience and strong technical backgrounds may move through the process more quickly, especially if scheduling aligns well for onsite interviews. The standard pace allows for a few days to a week between each stage, with the onsite or final round typically scheduled within a week of the technical interview. Fast-track candidates may receive an offer within days of the onsite discussion, while standard timelines may involve additional reference checks or follow-up conversations.
Next, let’s explore the specific types of questions you can expect at each stage of the Business Intelligence interview process.
3. Great American Insurance Group Business Intelligence Sample Interview Questions
3.1. SQL & Data Manipulation
Expect hands-on SQL questions that test your ability to extract, filter, and aggregate data for business reporting and analytics. You’ll need to demonstrate proficiency in writing clear, efficient queries and addressing data quality issues common in insurance and financial data. Be ready to explain your logic and handle edge cases.
3.1.1 Write a SQL query to count transactions filtered by several criterias.
Break down the requirements to filter transactions based on the specified conditions, then use aggregate functions to count the results. Explain your use of WHERE clauses and any joins or subqueries if needed.
3.1.2 Write a query to get the current salary for each employee after an ETL error.
Interpret the scenario to identify the logic needed to correct or select the most recent salary data, possibly using window functions or subqueries. Clearly justify your approach to resolving data inconsistencies.
3.1.3 Calculate total and average expenses for each department.
Group expense data by department, then use SUM and AVG functions to compute the desired metrics. Highlight any data cleansing steps if the dataset is not perfectly structured.
3.1.4 Calculate how much department spent during each quarter of 2023.
Extract the relevant date parts to determine quarters, group by department and quarter, and sum expenses. Discuss how you would handle missing or misaligned dates.
3.2. Data Warehousing & ETL
In this category, you’ll be asked to design and critique data pipelines and warehouse schemas for complex business scenarios. Focus on scalability, data quality, and alignment with business reporting needs. Be prepared to discuss trade-offs in your architectural decisions.
3.2.1 Design a data warehouse for a new online retailer.
Lay out the key fact and dimension tables, explain your normalization choices, and discuss how your design supports both transactional and analytical queries.
3.2.2 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Describe the steps for ingesting, cleaning, transforming, and loading data from multiple sources, addressing schema differences and data quality. Emphasize error handling and monitoring.
3.2.3 Let’s say that you're in charge of getting payment data into your internal data warehouse.
Outline your approach to pipeline design, focusing on data validation, incremental loads, and reconciliation processes to ensure completeness and accuracy.
3.2.4 Ensuring data quality within a complex ETL setup.
Discuss best practices for data validation, error logging, and automated quality checks in ETL workflows. Relate your answer to scenarios where multiple data sources and formats are involved.
3.3. Analytics Experimentation & Metrics
Here, you’ll encounter questions about designing experiments, measuring business impact, and defining KPIs. Show your ability to connect analytics to business outcomes, select appropriate metrics, and interpret results in a meaningful way.
3.3.1 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?
Propose a controlled experiment or A/B test, define success metrics (like retention, revenue, or lifetime value), and describe how you’d monitor and analyze results.
3.3.2 The role of A/B testing in measuring the success rate of an analytics experiment.
Explain the fundamentals of A/B testing, including hypothesis setup, randomization, and statistical significance. Illustrate how you’d interpret results and make business recommendations.
3.3.3 We're interested in how user activity affects user purchasing behavior.
Describe how you’d segment users, track activity and conversion events, and use statistical analysis to quantify the relationship. Mention controlling for confounding factors.
3.3.4 How would you measure the success of an email campaign?
List relevant KPIs (open rates, click-through, conversions), discuss how to design tests or cohorts, and explain how you’d attribute changes to the campaign.
3.4. Data Quality & Cleaning
Data quality is crucial in business intelligence, especially in regulated industries like insurance. Expect questions on identifying, diagnosing, and remediating data issues, as well as communicating data limitations to stakeholders.
3.4.1 How would you approach improving the quality of airline data?
Describe your process for profiling data, identifying common issues (nulls, duplicates, outliers), and implementing remediation steps. Explain how you’d prioritize fixes for business impact.
3.4.2 Describing a real-world data cleaning and organization project.
Walk through a specific example, covering the tools and techniques you used, challenges you faced, and how you validated the cleaned data.
3.4.3 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?
Detail your approach to data integration, joining disparate sources, resolving inconsistencies, and ensuring data integrity. Emphasize your analytical framework for extracting actionable insights.
3.5. Communication & Data Storytelling
Business intelligence roles require translating complex analyses into actionable insights for diverse audiences. You’ll need to demonstrate how you present findings, adapt to stakeholder needs, and make data accessible to non-technical users.
3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Describe your approach to tailoring content and visuals based on audience expertise, using analogies or business context to drive understanding.
3.5.2 Making data-driven insights actionable for those without technical expertise
Explain how you distill complex findings into clear recommendations, using storytelling, visuals, and business impact statements.
3.5.3 Demystifying data for non-technical users through visualization and clear communication
Discuss your strategies for building intuitive dashboards, choosing the right chart types, and ensuring stakeholders can self-serve key metrics.
3.6 Behavioral Questions
3.6.1 Tell me about a time you used data to make a decision.
Focus on a project where your analysis directly influenced a business outcome. Highlight the data you used, your recommendation, and the measurable impact.
3.6.2 Describe a challenging data project and how you handled it.
Share a specific project, the obstacles you faced (technical or organizational), and how you overcame them. Emphasize adaptability and resourcefulness.
3.6.3 How do you handle unclear requirements or ambiguity?
Explain your process for clarifying goals, asking questions, and iterating with stakeholders. Show that you’re proactive in reducing uncertainty.
3.6.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
Describe how you facilitated open dialogue, listened to feedback, and found common ground or a compromise.
3.6.5 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?
Explain how you quantified the additional effort, communicated trade-offs, and used a prioritization framework to align stakeholders.
3.6.6 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Share how you built trust, used evidence, and tailored your message to persuade decision-makers.
3.6.7 You’re given a dataset that’s full of duplicates, null values, and inconsistent formatting. The deadline is soon, but leadership wants insights from this data for tomorrow’s decision-making meeting. What do you do?
Describe your triage process: quickly profiling the data, prioritizing critical issues, and communicating data quality caveats alongside your analysis.
3.6.8 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Discuss the tools or scripts you built, how they improved efficiency, and the long-term impact on data reliability.
3.6.9 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Explain how you used early mock-ups to gather feedback, bridge gaps in understanding, and converge on a shared solution.
3.6.10 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Walk through your approach to handling missing data, your rationale for chosen methods, and how you communicated uncertainty in your results.
4. Preparation Tips for Great American Insurance Group Business Intelligence Interviews
4.1 Company-specific tips:
Familiarize yourself with the insurance industry and Great American Insurance Group’s unique position within it. Understand their focus on property and casualty insurance, niche markets, and tailored coverage solutions. Research how data-driven decision-making impacts risk assessment, claims management, and customer experience in the insurance sector.
Review recent news, annual reports, and strategic initiatives at Great American Insurance Group. Pay attention to their commitment to integrity, financial strength, and customer-centric values. Be ready to discuss how business intelligence can support these priorities, whether through operational efficiency, improved client service, or compliance.
Study the company’s approach to regulatory requirements and data privacy. Insurance organizations operate in highly regulated environments, so be prepared to speak to how you would ensure compliance and data security in your BI work.
4.2 Role-specific tips:
4.2.1 Practice designing and explaining dashboards that track insurance-specific metrics.
Prepare examples of dashboards you’ve built or could build to monitor claims frequency, loss ratios, premium trends, or customer retention. Be ready to discuss your process for gathering requirements from stakeholders and translating business needs into actionable visualizations.
4.2.2 Demonstrate proficiency in SQL, especially with complex joins, aggregations, and window functions.
Expect hands-on SQL questions, such as counting transactions by multiple criteria, correcting ETL errors, or calculating departmental expenses by quarter. Practice explaining your logic and handling edge cases common in insurance data, such as missing values or inconsistent date formats.
4.2.3 Show your understanding of data warehousing and scalable ETL pipeline design.
Be prepared to outline how you would architect a data warehouse for insurance operations, including fact and dimension tables relevant to policies, claims, and payments. Discuss your approach to designing ETL pipelines that ingest heterogeneous data, validate quality, and reconcile discrepancies.
4.2.4 Illustrate your ability to conduct analytics experimentation and define business metrics.
Practice framing controlled experiments, such as evaluating the impact of promotional campaigns or changes in underwriting criteria. Explain how you select KPIs, design A/B tests, and interpret results to inform business decisions.
4.2.5 Prepare to discuss your strategies for data cleaning and quality assurance.
Share specific examples of how you’ve profiled, cleaned, and validated complex datasets—especially those with duplicates, nulls, or inconsistent formatting. Emphasize your ability to prioritize fixes and communicate data limitations to stakeholders under tight deadlines.
4.2.6 Highlight your data storytelling and communication skills.
Be ready to demonstrate how you tailor presentations to different audiences, simplify complex findings, and make insights actionable for non-technical users. Discuss your approach to building intuitive dashboards and using clear language to drive understanding and business impact.
4.2.7 Reflect on your behavioral competencies, especially around stakeholder management, ambiguity, and influencing without authority.
Prepare stories that showcase your adaptability, negotiation skills, and ability to align cross-functional teams with data-driven recommendations. Practice articulating how you handle scope creep, conflicting priorities, and fast-paced decision-making environments.
4.2.8 Be ready to discuss automation and process improvement in BI workflows.
Share examples of automating data-quality checks, streamlining reporting processes, or building reusable templates that boost efficiency and reliability for business intelligence operations.
4.2.9 Select portfolio projects that demonstrate end-to-end BI impact.
If asked to present your work, choose projects that highlight your ability to extract, clean, analyze, and communicate insights that influenced business outcomes—especially those relevant to insurance or financial services.
4.2.10 Communicate your analytical trade-offs and decision-making process when working with imperfect data.
Practice explaining how you handle missing or messy data, the rationale behind your chosen methods, and how you ensure stakeholders understand the limitations and strengths of your analysis.
5. FAQs
5.1 “How hard is the Great American Insurance Group Business Intelligence interview?”
The interview is considered moderately challenging, especially for candidates new to the insurance industry. Expect a thorough evaluation of your technical skills in SQL, data warehousing, and dashboard design, as well as your ability to translate complex data into business insights. The process also places significant emphasis on communication, stakeholder management, and your understanding of insurance-specific analytics. Candidates who are well-prepared and can clearly articulate their problem-solving approach tend to do well.
5.2 “How many interview rounds does Great American Insurance Group have for Business Intelligence?”
Typically, there are 4 to 5 rounds: an initial application and resume review, a recruiter screen, a technical/case round, a behavioral interview, and a final onsite or virtual round. Some candidates may experience an additional follow-up or presentation round, especially for senior BI roles.
5.3 “Does Great American Insurance Group ask for take-home assignments for Business Intelligence?”
While not always required, some candidates may be given a take-home analytics case or a dashboard design exercise. This assignment usually focuses on real-world insurance data problems and assesses your ability to extract insights, visualize results, and communicate recommendations clearly.
5.4 “What skills are required for the Great American Insurance Group Business Intelligence?”
Key skills include advanced SQL, experience with data visualization tools (such as Tableau or Power BI), data warehousing concepts, ETL pipeline design, and strong analytical thinking. Additionally, the role demands excellent communication skills, stakeholder management, and the ability to work with insurance or financial datasets. Familiarity with regulatory and compliance requirements in the insurance sector is a plus.
5.5 “How long does the Great American Insurance Group Business Intelligence hiring process take?”
The typical process takes about 2 to 4 weeks from application to offer. Timelines may vary based on candidate availability, scheduling of onsite or virtual interviews, and the need for additional reference checks or presentations.
5.6 “What types of questions are asked in the Great American Insurance Group Business Intelligence interview?”
You can expect a mix of technical SQL problems, data warehousing and ETL pipeline design scenarios, analytics experimentation and business metrics cases, and questions on data quality and cleaning. There will also be behavioral questions focused on stakeholder management, communication, and problem-solving in ambiguous or high-pressure situations. Insurance-specific scenarios are common, so be prepared to discuss industry-relevant metrics and challenges.
5.7 “Does Great American Insurance Group give feedback after the Business Intelligence interview?”
Feedback is typically provided through the recruiter, especially for candidates who reach the later stages. While detailed technical feedback may be limited, you can expect to receive general insights about your interview performance and next steps.
5.8 “What is the acceptance rate for Great American Insurance Group Business Intelligence applicants?”
While specific numbers are not publicly available, the acceptance rate is competitive, reflecting the company’s high standards for technical proficiency, business acumen, and cultural fit. Well-prepared candidates with relevant experience have a strong chance of progressing through the process.
5.9 “Does Great American Insurance Group hire remote Business Intelligence positions?”
Yes, Great American Insurance Group offers remote and hybrid options for Business Intelligence positions, depending on the team’s needs and the specific role. Some roles may require occasional travel to the office or for team meetings, but there is flexibility for remote work arrangements.
6. Additional Resources
Great American Insurance Group Business Intelligence Ready to Ace Your Interview?
Ready to ace your Great American Insurance Group Business Intelligence interview? It’s not just about knowing the technical skills—you need to think like a Great American Insurance Group 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 Great American Insurance Group and similar companies.
With resources like the Great American Insurance Group 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.
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