Avant Business Intelligence Interview Guide

Getting ready for a Business Intelligence interview at Avant? The Avant Business Intelligence interview process typically spans a wide range of question topics and evaluates skills in areas like data analytics, SQL, data pipeline design, product metrics, and presenting actionable insights to diverse audiences. Interview preparation is especially important for this role at Avant, as candidates are expected to transform complex data from multiple sources into clear, strategic recommendations that drive business decisions and improve operational efficiency.

In preparing for the interview, you should:

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

Avant is a financial technology company specializing in providing personal loans and credit solutions to underserved consumers across the United States. Leveraging advanced analytics and innovative technology, Avant streamlines the borrowing process to offer fast, transparent, and accessible financial products. The company is committed to expanding financial opportunity and improving customer experience through data-driven decision-making. As a Business Intelligence professional, you will contribute to Avant’s mission by delivering actionable insights that drive operational efficiency and strategic growth.

As a Business Intelligence professional at Avant, you will be responsible for transforming data into actionable insights that inform business strategy and drive decision-making across the organization. This role involves gathering, analyzing, and visualizing data related to Avant’s financial products and customer behaviors. You will collaborate with cross-functional teams such as product, operations, and marketing to identify trends, measure performance, and recommend improvements. By developing dashboards, generating reports, and presenting findings to stakeholders, you help Avant optimize its lending services and support its mission to provide accessible financial solutions.

2.1 Stage 1: Application & Resume Review

The process begins with a thorough screening of your resume and application materials by the Avant talent acquisition team. They look for demonstrated experience with SQL, Python, analytics, and business intelligence best practices, as well as familiarity with product metrics and data-driven decision making. Highlighting hands-on experience with data pipelines, ETL processes, and dashboard development will help your application stand out. Tailor your resume to showcase measurable impact and cross-functional collaboration.

2.2 Stage 2: Recruiter Screen

This initial phone interview is typically conducted by a recruiter or HR representative. Expect a discussion of your background, motivation for joining Avant, and your experience in business intelligence roles. You may be asked to briefly explain past projects involving SQL, Python, and analytics, as well as how you’ve approached communicating complex insights to non-technical stakeholders. Preparation should focus on articulating your career narrative and aligning your skills with Avant’s mission and business model.

2.3 Stage 3: Technical/Case/Skills Round

During this stage, you’ll face a technical interview and a case assessment. Technical questions will test your proficiency in SQL (query writing, data manipulation, error handling), Python (data analysis, automation, and pipeline scripting), and analytics (product metrics, experiment design, and data interpretation). The case assessment, sometimes presented as a take-home assignment, will require you to solve a real-world business problem—often involving data cleaning, merging datasets, and extracting actionable insights. You may have 72 hours to complete this assignment, so plan for deep analytical thinking and clear, well-documented solutions.

2.4 Stage 4: Behavioral Interview

This round is designed to assess your interpersonal skills, problem-solving approach, and ability to collaborate across teams. Interviewers (often from the data team, product, or business units) will ask about your experience handling challenges in data projects, communicating insights to diverse audiences, and driving stakeholder engagement. Prepare to discuss situations where you’ve ensured data quality, managed competing priorities, or led cross-functional initiatives.

2.5 Stage 5: Final/Onsite Round

The final stage typically involves a panel interview with multiple team members, including business intelligence leaders and cross-functional partners. You’ll tackle a series of case studies and a coding test, focusing on advanced SQL queries, analytics scenarios, and business problem-solving. Expect questions about designing data warehouses, scaling ETL pipelines, interpreting product metrics, and presenting findings to executives. Demonstrating adaptability, technical rigor, and business acumen is key.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll receive an offer and move into discussions about compensation, benefits, and onboarding details with the recruiter. This is your opportunity to clarify role expectations, team structure, and growth opportunities within Avant.

2.7 Average Timeline

The Avant Business Intelligence interview process typically spans 3-5 weeks from initial application to offer. Fast-track candidates with highly relevant experience may complete the process in 2-3 weeks, while standard pacing involves a week between each stage. The take-home case assignment is usually allotted 2-4 days for completion, and onsite interviews are scheduled based on team availability.

Next, let’s break down the key interview questions you can expect at each stage.

3.1 SQL & Data Modeling

Expect questions that assess your ability to write efficient queries, design scalable data models, and handle complex ETL processes. Focus on demonstrating your proficiency with SQL, your understanding of data warehousing concepts, and your ability to ensure data quality and reliability.

3.1.1 Write a SQL query to count transactions filtered by several criterias.
Clarify the criteria and use appropriate WHERE clauses and aggregation. Mention how you optimize for performance and handle edge cases like nulls or duplicates.

Example answer: I’d filter the transactions table using the specified criteria, then use COUNT and GROUP BY to summarize the results, ensuring to exclude nulls and duplicates for accuracy.

3.1.2 Write a query to get the current salary for each employee after an ETL error.
Explain how you would identify and correct data inconsistencies caused by ETL failures, using window functions or subqueries to reconstruct the correct salary values.

Example answer: I’d use a window function to partition by employee and order by update timestamp, selecting the most recent non-error record to recover the correct salary.

3.1.3 Design a data warehouse for a new online retailer.
Discuss how you would approach schema design, dimensional modeling, and the ETL pipeline to support analytics and reporting for an online retailer.

Example answer: I’d implement a star schema with fact tables for orders and sales, dimension tables for products and customers, and design ETL jobs to ensure timely and accurate data ingestion.

3.1.4 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Describe your approach to handling diverse data formats, error handling, and scalability in ETL processes.

Example answer: I’d use modular ETL jobs with schema validation, error logging, and parallel processing to ingest partner data efficiently and maintain data integrity.

3.1.5 How would you approach improving the quality of airline data?
Explain your process for profiling, cleaning, and validating large datasets, and how you prioritize fixes for critical data quality issues.

Example answer: I’d start with data profiling to identify missing or inconsistent values, then develop automated cleaning scripts and validation checks to improve overall data reliability.

3.2 Product & Business Metrics

These questions evaluate your ability to define, track, and interpret metrics that drive business decisions. Emphasize your experience with A/B testing, segmentation, and aligning analytics with strategic goals.

3.2.1 Cheaper tiers drive volume, but higher tiers drive revenue. your task is to decide which segment we should focus on next.
Discuss how you would analyze segment performance using volume and revenue metrics, and recommend a data-driven strategy.

Example answer: I’d compare conversion rates, lifetime value, and churn across segments, then recommend focusing on the segment with the highest growth or profitability potential.

3.2.2 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?
Outline the steps for setting up the test, analyzing results, and using statistical techniques to ensure validity.

Example answer: I’d randomize users, collect conversion data, use bootstrap sampling to estimate confidence intervals, and apply statistical tests to determine significance.

3.2.3 The role of A/B testing in measuring the success rate of an analytics experiment.
Explain how you design experiments, select key metrics, and interpret results to guide business decisions.

Example answer: I’d define clear success metrics, use random assignment, monitor experiment progress, and analyze results using statistical tests to measure impact.

3.2.4 Which metrics and visualizations would you prioritize for a CEO-facing dashboard during a major rider acquisition campaign?
Describe your process for selecting high-impact metrics and creating executive-friendly visualizations.

Example answer: I’d focus on KPIs like new riders, retention rates, and cost per acquisition, using concise charts and trend lines for quick executive insights.

3.2.5 Write a query to find all users that were at some point "Excited" and have never been "Bored" with a campaign.
Demonstrate your ability to segment users based on behavioral event logs using conditional aggregation.

Example answer: I’d aggregate user events, filtering for those with “Excited” and excluding any with “Bored,” to identify the target group.

3.3 Data Analysis & Visualization

Avant emphasizes clear communication of complex insights. You’ll be tested on your ability to make data accessible, actionable, and tailored for different audiences.

3.3.1 Making data-driven insights actionable for those without technical expertise
Describe how you translate complex findings into actionable recommendations for non-technical stakeholders.

Example answer: I use analogies, focus on business outcomes, and present key takeaways with visual aids, avoiding jargon.

3.3.2 How to present complex data insights with clarity and adaptability tailored to a specific audience
Explain your approach to customizing presentations based on audience needs.

Example answer: I assess audience familiarity, use clear visuals, and adjust the level of technical detail to ensure comprehension.

3.3.3 Demystifying data for non-technical users through visualization and clear communication
Discuss techniques for creating intuitive dashboards and visualizations.

Example answer: I leverage interactive dashboards, use color coding for emphasis, and include annotations to guide interpretation.

3.3.4 How would you visualize data with long tail text to effectively convey its characteristics and help extract actionable insights?
Explain your strategy for handling and visualizing text-heavy datasets.

Example answer: I use word clouds, frequency distributions, and clustering techniques to highlight patterns and key insights.

3.3.5 Designing a dynamic sales dashboard to track McDonald's branch performance in real-time
Describe your process for building a real-time dashboard with actionable metrics.

Example answer: I’d integrate live data feeds, use filters for branch-level analysis, and prioritize metrics like sales, growth rate, and customer feedback.

3.4 Data Engineering & System Design

You will be expected to demonstrate your understanding of scalable systems, ETL pipelines, and handling large-scale data challenges. Show your familiarity with design principles and data integrity.

3.4.1 Let's say that you're in charge of getting payment data into your internal data warehouse.
Describe your approach to designing a robust data pipeline for payment data.

Example answer: I’d build modular ETL jobs, implement validation checks, and design for scalability to handle growing transaction volume.

3.4.2 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Explain your pipeline design from data ingestion to model deployment.

Example answer: I’d automate data collection, perform feature engineering, and deploy predictive models with real-time monitoring.

3.4.3 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints.
Discuss your selection of open-source tools and strategies for cost-effective reporting.

Example answer: I’d use tools like Apache Airflow, PostgreSQL, and Metabase, ensuring scalability and maintainability within budget.

3.4.4 Design a data warehouse for a e-commerce company looking to expand internationally?
Describe your approach to supporting multi-region data and international analytics.

Example answer: I’d design with localization, scalable architecture, and region-specific dimensions for robust global reporting.

3.4.5 Modifying a billion rows
Explain strategies for efficiently updating massive datasets.

Example answer: I’d use batch processing, partitioning, and incremental updates to minimize downtime and resource usage.

3.5 Behavioral Questions (Continue the numbering from above for H3 texts)

3.5.1 Tell me about a time you used data to make a decision.
Highlight how your analysis led to a specific business action or outcome, emphasizing the impact and your communication with stakeholders.

3.5.2 Describe a challenging data project and how you handled it.
Focus on the complexity, your problem-solving approach, and the lessons learned.

3.5.3 How do you handle unclear requirements or ambiguity?
Discuss your process for clarifying goals, collaborating with stakeholders, and iterating on solutions.

3.5.4 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Share your strategy for building consensus and demonstrating value through data.

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?
Explain your prioritization framework and communication techniques for managing expectations.

3.5.6 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?
Detail your triage process, focusing on high-impact fixes and transparent communication about data quality.

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

3.5.8 How have you balanced speed versus rigor when leadership needed a “directional” answer by tomorrow?
Discuss your approach to delivering timely insights while maintaining transparency about limitations.

3.5.9 Tell us about a time you caught an error in your analysis after sharing results. What did you do next?
Explain how you addressed the error, communicated with stakeholders, and implemented safeguards to prevent recurrence.

3.5.10 Describe how you prioritized backlog items when multiple executives marked their requests as “high priority.”
Share your framework for prioritization and how you ensured alignment with business goals.

4.1 Company-specific tips:

Familiarize yourself with Avant’s mission to expand financial opportunity for underserved consumers. Understand the company’s approach to personal loans and credit solutions, with an emphasis on speed, transparency, and data-driven decision-making. Research Avant’s product offerings and recent initiatives, paying close attention to how analytics and business intelligence support operational efficiency and customer experience.

Review Avant’s core business metrics, such as loan approval rates, default risk scores, customer acquisition costs, and lifetime value. Be prepared to discuss how these metrics inform strategic decisions, product enhancements, and risk management practices.

Explore Avant’s technology stack and data infrastructure. Although specifics may not be public, anticipate questions about scalable data pipelines, ETL processes, and how Avant might leverage advanced analytics to optimize lending and credit operations.

Understand the importance of cross-functional collaboration at Avant. Business Intelligence professionals regularly partner with product, operations, and marketing teams, so be ready to demonstrate your experience in driving alignment and delivering insights across diverse stakeholders.

4.2 Role-specific tips:

4.2.1 Practice writing advanced SQL queries to analyze transactional and customer data.
Avant’s Business Intelligence interviews often focus on your ability to extract insights from complex datasets. Sharpen your SQL skills by working on queries involving multi-table joins, window functions, and aggregation—especially those that filter transactions, identify data inconsistencies, and summarize product performance. Show that you can handle edge cases like nulls, duplicates, and ETL errors.

4.2.2 Prepare to design scalable data pipelines and data warehouses tailored to financial products.
Expect questions about building robust ETL pipelines and designing data warehouses that support analytics for lending and credit products. Be ready to discuss schema design, dimensional modeling, and strategies for ingesting heterogeneous data sources. Highlight your experience with modular pipeline architecture, error handling, and ensuring data integrity at scale.

4.2.3 Demonstrate your ability to define, track, and interpret key business metrics.
Business Intelligence at Avant is driven by actionable metrics. Practice articulating how you select, calculate, and visualize KPIs such as conversion rates, acquisition costs, retention, and lifetime value. Be prepared to recommend metric frameworks for executive dashboards, especially during product launches or acquisition campaigns.

4.2.4 Show proficiency in designing and analyzing A/B tests for product and process improvements.
Avant values experimentation and data-driven decision-making. Review the principles of A/B testing, including experiment setup, randomization, and statistical analysis. Be ready to explain how you would use bootstrap sampling to calculate confidence intervals and ensure statistical validity. Discuss how you interpret results and translate findings into recommendations.

4.2.5 Practice presenting complex insights to non-technical stakeholders with clarity and impact.
You’ll often be tasked with communicating technical findings to executives and cross-functional teams. Refine your ability to distill complex analyses into clear, actionable recommendations. Use analogies, focus on business outcomes, and leverage intuitive visualizations to make data accessible for all audiences.

4.2.6 Highlight your experience with data cleaning and quality assurance under tight deadlines.
Avant’s fast-paced environment requires quick turnaround on data projects. Be ready to describe your triage process for messy datasets—prioritizing high-impact fixes like removing duplicates, handling nulls, and standardizing formats. Emphasize your ability to deliver reliable insights while communicating transparently about data limitations.

4.2.7 Discuss your approach to automating data-quality checks and maintaining data reliability.
Showcase examples of automating recurrent tasks, such as data validation scripts or monitoring pipelines. Explain how these solutions improved efficiency, reduced errors, and supported scalable analytics.

4.2.8 Be prepared to balance speed and rigor when delivering insights for urgent business decisions.
Sometimes leadership needs a directional answer quickly. Practice framing your analyses to highlight both actionable recommendations and the limitations of fast-turnaround data work. Demonstrate your judgment in when to prioritize speed versus thoroughness.

4.2.9 Illustrate your ability to handle ambiguity and prioritize competing requests.
Avant values adaptability and business alignment. Discuss your strategies for clarifying requirements, managing scope creep, and prioritizing backlog items when multiple executives mark their requests as urgent. Explain your frameworks for balancing impact, feasibility, and strategic alignment.

4.2.10 Reflect on your experience influencing stakeholders and driving adoption of data-driven recommendations.
Share stories where you built consensus and demonstrated the business value of analytics, even without formal authority. Emphasize your communication skills and ability to tailor your message for different audiences.

5.1 “How hard is the Avant Business Intelligence interview?”
The Avant Business Intelligence interview is considered moderately challenging, especially for candidates who may not have prior fintech or high-growth startup experience. The process is rigorous in its assessment of both technical skills—like advanced SQL, data pipeline design, and analytics—and business acumen, including the ability to translate data into strategic recommendations. Candidates who excel are those who can demonstrate hands-on experience with real-world data problems, articulate their insights clearly, and adapt to ambiguous, fast-paced environments.

5.2 “How many interview rounds does Avant have for Business Intelligence?”
Avant’s Business Intelligence interview process typically consists of five to six rounds. These include an initial application and resume review, a recruiter screen, a technical/case/skills round (which may include a take-home assignment), a behavioral interview, and a final onsite or virtual panel interview. Each stage is designed to evaluate a unique aspect of your business intelligence skill set, from technical proficiency to cross-functional communication and business impact.

5.3 “Does Avant ask for take-home assignments for Business Intelligence?”
Yes, Avant frequently includes a take-home assignment as part of the technical interview round for Business Intelligence candidates. The assignment is usually a real-world business case that requires you to clean, analyze, and interpret a dataset, then present actionable insights. You can expect to spend 2-4 days on this task, and your ability to communicate findings to both technical and non-technical stakeholders will be closely evaluated.

5.4 “What skills are required for the Avant Business Intelligence?”
Core skills for Avant’s Business Intelligence role include strong SQL expertise, data analysis using Python or similar tools, experience with ETL pipeline design, and a solid grasp of data modeling and warehousing concepts. You should also be adept at defining and tracking business metrics, designing and interpreting A/B tests, and presenting complex insights in a clear, business-focused manner. Communication, stakeholder management, and the ability to thrive in a fast-paced, data-driven environment are just as important as technical skills.

5.5 “How long does the Avant Business Intelligence hiring process take?”
The typical Avant Business Intelligence hiring process takes about 3-5 weeks from application to offer. Fast-track candidates with highly relevant experience may complete the process in as little as 2-3 weeks, while standard timelines allow for about a week between each interview round. The take-home assignment usually has a 2-4 day completion window, and final interviews are scheduled based on candidate and team availability.

5.6 “What types of questions are asked in the Avant Business Intelligence interview?”
You can expect a mix of technical and business-focused questions. Technical questions will test your SQL skills, data modeling, ETL pipeline design, and experience with analytics tools. Case questions may cover product metrics, A/B testing, and scenario-based problem solving. You’ll also face behavioral questions that assess your ability to manage ambiguity, collaborate across teams, and communicate data-driven recommendations to executives and non-technical stakeholders.

5.7 “Does Avant give feedback after the Business Intelligence interview?”
Avant generally provides feedback to candidates after each stage of the Business Intelligence interview process, particularly through the recruiter. While you may receive high-level feedback regarding your fit or performance, detailed technical feedback is less common due to company policy. However, you can always request additional insights or clarification through your recruiter.

5.8 “What is the acceptance rate for Avant Business Intelligence applicants?”
While Avant does not publish official acceptance rates, the Business Intelligence role is competitive. Based on industry benchmarks and candidate reports, the estimated acceptance rate is around 3-6% for qualified applicants. Standing out requires a strong combination of technical expertise, business insight, and adaptability.

5.9 “Does Avant hire remote Business Intelligence positions?”
Avant does offer remote opportunities for Business Intelligence roles, though availability may depend on current business needs and specific team requirements. Some positions may be fully remote, while others could require occasional in-person collaboration at Avant’s offices. Be sure to clarify remote work expectations with your recruiter during the interview process.

• Related guides:
  • Avant interview questions
  • Business Intelligence interview guide
  • “Top Business Intelligence interview tips”
  • Top 12 Business Intelligence Case Studies (Updated in 2025)
  • How to Prepare for Business Intelligence Interviews: Success Story

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

With resources like the Avant 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 topics like advanced SQL, scalable ETL pipeline design, product metrics, and presenting actionable insights to executives—each aligned to the challenges and expectations you’ll face at Avant.

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!