Erpa Business Intelligence Interview Guide

Getting ready for a Business Intelligence interview at Erpa? The Erpa Business Intelligence interview process typically spans 4–6 question topics and evaluates skills in areas like data warehousing, analytics strategy, stakeholder communications, and translating complex data into actionable business insights. Interview preparation is especially important for this role at Erpa, as candidates are expected to design robust data solutions, build clear dashboards, and present findings in ways that drive decision-making across diverse business units. Given Erpa's emphasis on data-driven transformation, being able to demonstrate both technical expertise and adaptability in communicating with technical and non-technical audiences is crucial.

In preparing for the interview, you should:

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

Erpa is an Oracle Platinum Partner specializing in Oracle product licensing, implementations, upgrades, and production support for PeopleSoft, Fusion Applications, Fusion Middleware, EPM, Business Intelligence, and Identity Management solutions. Headquartered in Dublin, Ohio, Erpa serves mid-market and Fortune 500 clients, delivering high-quality Oracle consulting services since 1999. With over 250 employees—100% focused on PeopleSoft and Oracle technologies and over 60% Oracle certified—the company emphasizes a client-centric approach and proven expertise. As a Business Intelligence professional, you will contribute to Erpa’s reputation for excellence in delivering impactful, data-driven Oracle solutions.

As a Business Intelligence professional at Erpa, you are responsible for gathering, analyzing, and interpreting complex data to provide strategic insights that drive business decisions. You will work closely with various departments to design and develop dashboards, reports, and visualizations that highlight key performance metrics and operational trends. Typical tasks include data modeling, report automation, and collaborating with stakeholders to identify opportunities for process improvement. Your contributions help Erpa optimize business processes, support data-driven decision-making, and enhance overall company efficiency. This role is essential in transforming raw data into actionable intelligence that aligns with Erpa’s business objectives.

2.1 Stage 1: Application & Resume Review

The process begins with a detailed review of your application and resume by the Erpa talent acquisition team. They look for demonstrated experience with business intelligence tools, data warehousing, ETL pipelines, and a track record of generating actionable insights from complex datasets. Evidence of effective stakeholder communication, cross-functional collaboration, and experience in fraud detection or e-commerce analytics can further strengthen your profile. Tailor your resume to highlight relevant BI projects, technical skills (such as SQL, data modeling, and dashboarding), and your impact on previous business outcomes.

2.2 Stage 2: Recruiter Screen

Next, a recruiter will reach out for a 20-30 minute phone conversation. This call covers your background, motivation for joining Erpa, and alignment with the company’s values and culture. Expect to discuss your understanding of the business intelligence function, your approach to data-driven problem solving, and why you want to work in this environment. Preparation should include concise summaries of your experience, knowledge of Erpa’s business model, and examples of how your values align with the company’s mission.

2.3 Stage 3: Technical/Case/Skills Round

This stage typically involves one or two interviews (virtual or onsite) with BI leads or senior data professionals. You’ll encounter technical questions and case studies focused on data warehouse design, ETL pipeline architecture, SQL query writing, and analytics problem-solving. Scenarios may include designing scalable reporting solutions, evaluating the impact of business promotions, or addressing data quality within complex ETL setups. You may also be asked to interpret business metrics, analyze multi-source datasets, and demonstrate your ability to communicate insights to both technical and non-technical stakeholders. Preparation should focus on reviewing core BI concepts, practicing clear and structured explanations, and being ready to walk through your approach to real-world data challenges.

2.4 Stage 4: Behavioral Interview

In this round, you’ll meet with a hiring manager or cross-functional partner. The focus is on your soft skills, such as stakeholder management, adaptability, and your approach to overcoming project hurdles. You’ll be asked to discuss past experiences—such as resolving misaligned expectations, leading a BI project, or making data accessible to non-technical users. Prepare by reflecting on stories that showcase your leadership, collaboration, and ability to drive business value through analytics.

2.5 Stage 5: Final/Onsite Round

The final stage often involves a panel interview or a series of back-to-back meetings with key team members, including BI directors, analytics managers, and business stakeholders. You may be asked to present a past project, walk through a case study, or respond to real-time problem-solving scenarios. Emphasis is placed on your ability to synthesize complex data, present actionable insights, and tailor your communication to diverse audiences. This is also an opportunity for you to ask questions about Erpa’s BI strategy, team culture, and growth opportunities.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll receive a verbal offer from the recruiter, followed by a formal written offer. This stage includes discussions about compensation, benefits, and start date. Be prepared to negotiate based on your market research and to communicate your expectations professionally and transparently.

2.7 Average Timeline

The Erpa Business Intelligence interview process typically spans 3-5 weeks from application to offer. Fast-track candidates with highly relevant experience or internal referrals may complete the process in as little as 2-3 weeks, while the standard pace allows for scheduling flexibility between rounds and thorough assessment by multiple stakeholders. Take-home assignments, if included, usually have a 3-5 day deadline, and panel interviews are scheduled based on team availability.

Now, let’s dive into the specific interview questions you might encounter throughout the Erpa Business Intelligence interview process.

3.1 Data Warehousing & ETL

Data warehousing and ETL are core skills for Business Intelligence roles at Erpa, as they enable robust, scalable, and reliable data management. You’ll be expected to design data pipelines, manage data from multiple sources, and ensure high data quality. Focus on demonstrating your ability to architect solutions that support analytics and business reporting.

3.1.1 Design a data warehouse for a new online retailer
Describe your approach to schema design, data modeling (star vs. snowflake), and how you’d handle incremental loads and slowly changing dimensions. Emphasize scalability, data quality, and business use cases.

3.1.2 How would you design a data warehouse for a e-commerce company looking to expand internationally?
Discuss handling localization (currencies, languages), partitioning by region, and supporting country-specific regulations. Highlight your strategy for maintaining consistency across global datasets.

3.1.3 Ensuring data quality within a complex ETL setup
Explain how you’d implement validation, monitoring, and error handling in ETL pipelines. Discuss tools or frameworks you’d use for data profiling and automated alerts.

3.1.4 Let's say that you're in charge of getting payment data into your internal data warehouse.
Outline the end-to-end pipeline for ingesting, cleaning, and storing payment data, with attention to data integrity, security, and compliance. Mention how you’d handle schema evolution and downstream analytics needs.

3.2 Data Analytics & Business Impact

This category assesses your ability to turn raw data into actionable business insights. You’ll need to demonstrate how you analyze user behavior, measure business health, and communicate recommendations that drive impact.

3.2.1 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?
Describe the experimental setup (A/B test), key metrics (incremental revenue, retention, fraud detection), and how you’d monitor for unintended consequences.

3.2.2 Cheaper tiers drive volume, but higher tiers drive revenue. your task is to decide which segment we should focus on next.
Discuss how you’d analyze customer segments, calculate lifetime value, and recommend a data-driven strategy for prioritization.

3.2.3 *We're interested in how user activity affects user purchasing behavior. *
Explain how you’d use cohort analysis or regression modeling to link activity patterns to conversions, and how you’d use these insights to inform product or marketing decisions.

3.2.4 How would you measure the success of an online marketplace introducing an audio chat feature given a dataset of their usage?
Identify success metrics (engagement, retention, conversion), describe your approach to A/B testing or pre-post analysis, and discuss how you’d account for confounding factors.

3.3 Data Visualization & Communication

Effective communication of insights is essential in BI roles at Erpa, especially when working with non-technical stakeholders. You’ll be evaluated on your ability to translate complex data into clear, actionable recommendations.

3.3.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss your process for audience analysis, selecting the right visualizations, and adapting your message to business or technical audiences.

3.3.2 Making data-driven insights actionable for those without technical expertise
Explain how you break down technical jargon, use analogies, and focus on business value to ensure your message resonates.

3.3.3 Demystifying data for non-technical users through visualization and clear communication
Describe your approach to dashboard design, using color, layout, and interactivity to maximize accessibility and impact.

3.3.4 How would you visualize data with long tail text to effectively convey its characteristics and help extract actionable insights?
Share techniques for summarizing, clustering, or highlighting outliers in textual data, and discuss how to make insights actionable.

3.4 Data Integration & System Design

Business Intelligence at Erpa often involves integrating disparate data sources and designing systems that are robust and scalable. These questions test your architectural thinking and ability to solve real-world data engineering challenges.

3.4.1 Design a system to synchronize two continuously updated, schema-different hotel inventory databases at Agoda.
Discuss strategies for schema mapping, conflict resolution, and ensuring data consistency in near real-time.

3.4.2 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?
Outline your end-to-end process: data profiling, cleaning, joining, and extracting insights while ensuring data quality and compliance with company values around fraud detection and security.

3.4.3 Write a SQL query to count transactions filtered by several criterias.
Describe your approach to writing efficient, scalable SQL queries, including filtering, grouping, and handling edge cases.

3.4.4 Write a query to compute the average time it takes for each user to respond to the previous system message
Explain how you’d use window functions and time calculations, and clarify assumptions about missing or unordered data.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision.
Share a specific example where your analysis directly influenced a business outcome. Emphasize your thought process, the data you used, and the measurable impact.

3.5.2 Describe a challenging data project and how you handled it.
Discuss the scope, obstacles, and how you overcame them—especially if it involved ambiguous requirements, tight deadlines, or messy data.

3.5.3 How do you handle unclear requirements or ambiguity?
Explain your approach to clarifying goals, collaborating with stakeholders, and iterating on solutions when the project scope is not well-defined.

3.5.4 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?
Highlight your communication skills, openness to feedback, and methods for building consensus.

3.5.5 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Describe the situation, your strategy for bridging the communication gap, and the outcome.

3.5.6 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a dashboard quickly.
Discuss trade-offs you considered and how you ensured quality while meeting business needs.

3.5.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Focus on your ability to build trust, use evidence, and tailor your message to different audiences.

3.5.8 Tell us about a time you caught an error in your analysis after sharing results. What did you do next?
Share how you identified the error, communicated transparently, and took corrective action to maintain trust.

3.5.9 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Explain your process for investigating discrepancies, validating data sources, and documenting your decision.

4.1 Company-specific tips:

Familiarize yourself with Erpa’s client-centric approach and commitment to excellence in Oracle and PeopleSoft solutions. Research the company’s history, values, and its reputation for high-quality consulting, especially in the context of business transformation through data. Be ready to discuss how your own values align with Erpa’s—showcase examples where you prioritized integrity, collaboration, and customer satisfaction in your work.

Understand Erpa’s core business areas, particularly their focus on Oracle product licensing, implementations, and support. Demonstrate awareness of how business intelligence supports these services, such as optimizing product rollouts, tracking project KPIs, or improving operational efficiency for clients. Reference recent trends in Oracle BI solutions or major industry shifts that affect Erpa’s service offerings.

Show that you appreciate the importance of fraud detection and security in Erpa’s client engagements, especially when handling payment data. Be prepared to discuss your experience with fraud analytics, referencing relevant scenarios like afterpay scam detection or payment fraud monitoring. Highlight your understanding of how business intelligence can proactively identify anomalies and protect client interests.

Review the typical interview structure and timeline at Erpa. Prepare to articulate why Erpa’s interview process appeals to you, and express your enthusiasm for working in a rigorous, multi-stage evaluation environment. Mention your adaptability and readiness to engage with technical and non-technical stakeholders throughout the process.

4.2 Role-specific tips:

4.2.1 Practice communicating complex analytics to non-technical stakeholders.
Develop clear, concise explanations for technical concepts, such as data warehousing, ETL pipelines, and fraud detection algorithms. Use real-world analogies and focus on actionable business outcomes to ensure your insights resonate with diverse audiences. Prepare stories that demonstrate your ability to bridge the gap between data and decision-making.

4.2.2 Prepare to discuss payment and fraud detection use cases, including afterpay scams.
Review scenarios where you analyzed payment transaction data to identify fraudulent patterns or scams. Be ready to detail your approach to building detection models, validating suspicious activity, and collaborating with compliance teams. Reference metrics and business impact to show how your work contributed to reducing fraud and safeguarding revenue.

4.2.3 Demonstrate proficiency in designing scalable data warehouses and robust ETL pipelines.
Practice articulating your strategy for schema design, incremental data loads, and handling slowly changing dimensions. Discuss how you ensure data quality and integrity, especially in environments where multiple payment systems or international data sources are involved. Highlight any experience integrating disparate datasets, such as those from afterpay or paypay platforms.

4.2.4 Show expertise in building dashboards and visualizations tailored for business impact.
Prepare examples of dashboards you’ve built that track key performance indicators, fraud metrics, or operational trends. Emphasize your process for selecting the right visualization techniques, adapting layouts for different audiences, and making complex data accessible. Discuss how your dashboards have driven actionable decisions or improved stakeholder understanding.

4.2.5 Be ready to analyze and synthesize data from multiple sources, including payment, user behavior, and fraud logs.
Outline your end-to-end approach to data profiling, cleaning, joining, and extracting insights from diverse datasets. Discuss how you ensure compliance with company values—such as accuracy, transparency, and security—when working with sensitive financial data. Share examples of how you’ve used multi-source analytics to uncover trends or optimize business processes.

4.2.6 Prepare to answer behavioral questions with a focus on stakeholder management and adaptability.
Reflect on experiences where you navigated ambiguous requirements, resolved data discrepancies, or influenced teams without formal authority. Emphasize your problem-solving skills, ability to communicate under pressure, and commitment to maintaining data integrity—even when faced with tight deadlines or conflicting priorities.

4.2.7 Practice writing and explaining SQL queries for real-world business scenarios.
Review your skills in filtering, grouping, and joining tables to answer business questions, such as counting transactions or calculating average response times. Be ready to walk through your query logic step-by-step, clarifying assumptions and addressing edge cases, especially when dealing with payment or fraud detection data.

4.2.8 Prepare thoughtful questions about Erpa’s BI strategy, growth opportunities, and team culture.
Show genuine interest in how Erpa leverages business intelligence to drive client success. Ask about recent BI initiatives, data governance practices, or opportunities to innovate within the team. Demonstrate your curiosity and eagerness to contribute to Erpa’s ongoing transformation.

5.1 How hard is the Erpa Business Intelligence interview?
The Erpa Business Intelligence interview is challenging but highly rewarding for candidates who are well-prepared. You’ll be tested on your ability to design scalable data solutions, analyze complex business scenarios, and communicate insights to both technical and non-technical stakeholders. Expect in-depth technical questions on data warehousing, ETL, and analytics, alongside behavioral scenarios that assess your adaptability and alignment with Erpa’s values—such as integrity and client-centricity. Experience with fraud detection, especially in payment systems like afterpay or paypay, is a plus.

5.2 How many interview rounds does Erpa have for Business Intelligence?
Erpa’s Business Intelligence interview process typically consists of 4–6 rounds. These include an initial recruiter screen, one or two technical/case interviews, a behavioral interview, and a final onsite or panel round with key team members. Some candidates may also complete a take-home assignment, depending on the team’s requirements.

5.3 Does Erpa ask for take-home assignments for Business Intelligence?
Yes, Erpa may include a take-home assignment as part of the Business Intelligence interview process. Assignments usually focus on real-world analytics problems, such as designing a dashboard, analyzing payment data for fraud detection, or developing a scalable ETL pipeline. You’ll generally have 3–5 days to complete the task, allowing you to showcase your technical skills and business acumen.

5.4 What skills are required for the Erpa Business Intelligence?
Key skills for Erpa Business Intelligence roles include expertise in data warehousing, ETL pipeline design, SQL, and dashboard development. Strong analytical thinking, the ability to synthesize insights from diverse datasets (including payment and fraud detection logs), and excellent stakeholder communication are essential. Familiarity with e-commerce analytics, fraud prevention strategies (such as afterpay scam detection), and the ability to uphold company values in data-driven decision-making will set you apart.

5.5 How long does the Erpa Business Intelligence hiring process take?
The typical timeline for the Erpa Business Intelligence hiring process is 3–5 weeks from application to offer. Fast-track candidates or those with internal referrals may move through the process in as little as 2–3 weeks. Scheduling flexibility and thorough assessment by multiple stakeholders are prioritized, so the pace may vary depending on availability.

5.6 What types of questions are asked in the Erpa Business Intelligence interview?
Expect a blend of technical and behavioral questions. Technical topics include data warehouse design, ETL architecture, SQL challenges, analytics case studies (often involving payment and fraud detection), and dashboarding. Behavioral questions focus on stakeholder management, adaptability, and alignment with Erpa’s values—such as transparency, integrity, and client satisfaction. You may also encounter scenarios related to afterpay scam detection, payment data analysis, and handling ambiguous requirements.

5.7 Does Erpa give feedback after the Business Intelligence interview?
Erpa typically provides high-level feedback through recruiters, especially after final rounds. While detailed technical feedback may be limited, you can expect insights into your performance and fit for the role. If you complete a take-home assignment, feedback is often focused on your approach and business impact.

5.8 What is the acceptance rate for Erpa Business Intelligence applicants?
Erpa’s Business Intelligence positions are competitive, with an estimated acceptance rate of 3–7% for qualified applicants. The company seeks candidates who demonstrate technical excellence, strong business acumen, and a clear alignment with Erpa’s values and client-focused approach.

5.9 Does Erpa hire remote Business Intelligence positions?
Yes, Erpa offers remote opportunities for Business Intelligence professionals, depending on project requirements and client needs. Some roles may require occasional travel or onsite collaboration, but Erpa supports flexible work arrangements to attract top talent and promote work-life balance.

• Related guides:
  • Erpa interview questions
  • Business Intelligence interview guide
  • Top business intelligence interview tips

Ready to ace your Erpa Business Intelligence interview? It’s not just about knowing the technical skills—you need to think like an Erpa Business Intelligence professional, solve problems under pressure, and connect your expertise to real business impact. Whether you’re navigating scenarios involving afterpay scam detection, aligning with Erpa’s core values, or showcasing your experience in payment and fraud analytics, demonstrating both your technical depth and your understanding of the company’s mission is key. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at Erpa and similar companies.

With resources like the Erpa 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. You’ll be able to practice questions that mirror the Erpa interview process, including payment analytics, afterpay fraud detection, and strategies for effective stakeholder communication.

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!