Epic Business Intelligence Interview Guide

Getting ready for a Business Intelligence interview at Epic? The Epic Business Intelligence interview process typically spans a wide range of question topics and evaluates skills in areas like data modeling, analytics, dashboard design, stakeholder communication, and experiment measurement. Interview prep is especially important for this role at Epic, as candidates are expected to demonstrate their ability to transform complex data into actionable insights, design scalable data solutions, and communicate findings effectively to both technical and non-technical audiences within a dynamic healthcare technology environment.

In preparing for the interview, you should:

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

Epic is a global leader in healthcare software, providing solutions that help care for over 190 million people worldwide. The company partners with top healthcare organizations and research institutions to develop, implement, and support software that reduces medical errors, improves disease screening, and enhances patient care quality. Privately held and employee-owned since its founding in 1979, Epic employs over 9,000 professionals dedicated to advancing healthcare technology and outcomes. As a Business Intelligence professional, you will contribute to Epic’s mission by leveraging data to inform decision-making and drive improvements across the healthcare industry.

As a Business Intelligence professional at Epic, you are responsible for transforming complex healthcare and operational data into actionable insights that support strategic decision-making across the organization. You will work closely with cross-functional teams to design, build, and maintain data models, dashboards, and reports that inform process improvements and business outcomes. Key responsibilities include analyzing data trends, identifying opportunities for efficiency, and presenting findings to stakeholders to guide organizational strategy. This role is essential in helping Epic leverage data to enhance healthcare software solutions and drive better outcomes for clients and patients.

2.1 Stage 1: Application & Resume Review

The process begins with a thorough review of your application and resume by Epic’s talent acquisition team. They focus on your experience with data analytics, business intelligence platforms, SQL proficiency, data warehousing, ETL pipeline design, and your ability to translate complex insights into actionable business decisions. Emphasize measurable impact, cross-functional collaboration, and experience with stakeholder communication in your resume to stand out. Preparation involves tailoring your resume to showcase relevant business intelligence projects, technical skills, and leadership in driving data-driven outcomes.

2.2 Stage 2: Recruiter Screen

A recruiter will conduct a 30-45 minute introductory call to discuss your background, motivation for joining Epic, and alignment with the company’s mission. Expect questions about your experience with BI tools, how you communicate insights to non-technical stakeholders, and your approach to solving business problems using data. Preparation should include clear examples of your business intelligence work, familiarity with Epic’s products and culture, and concise articulation of your career trajectory.

2.3 Stage 3: Technical/Case/Skills Round

This stage typically consists of one or two interviews led by a BI team manager or senior analyst. You’ll be asked to solve technical problems related to SQL, data modeling, and pipeline design, as well as case studies involving data-driven decision making, dashboard creation, and experiment analysis (such as A/B testing or causal inference). Expect hands-on exercises involving data cleaning, aggregation, and visualization, as well as system design questions for data warehouses or reporting pipelines. Preparation should focus on demonstrating your ability to build scalable BI solutions, analyze diverse data sources, and communicate findings effectively.

2.4 Stage 4: Behavioral Interview

A hiring manager or team lead will assess your interpersonal and problem-solving skills through scenario-based and behavioral questions. You’ll discuss challenges faced in past data projects, stakeholder management, handling disagreements, and adapting communication for technical and non-technical audiences. Be prepared to reflect on your strengths, weaknesses, and strategies for overcoming project hurdles. Preparation involves practicing concise storytelling about your role in overcoming obstacles, driving cross-functional alignment, and ensuring data quality.

2.5 Stage 5: Final/Onsite Round

The final round may include multiple interviews with BI team members, product managers, and business partners. This stage evaluates your strategic thinking, presentation skills, and ability to synthesize insights for executive audiences. You may be asked to present a sample analysis, design a dashboard, or walk through the end-to-end architecture of a BI solution. Expect deeper dives into your technical acumen and your approach to driving business outcomes through analytics. Preparation should center on structuring presentations, demonstrating adaptability, and showcasing your impact on business metrics.

2.6 Stage 6: Offer & Negotiation

Once you’ve successfully completed the interviews, the recruiter will reach out to discuss the offer, compensation package, and potential team placement. This stage is typically handled by the talent acquisition team in coordination with BI leadership. Preparation involves researching market compensation benchmarks, clarifying role expectations, and preparing to negotiate based on your experience and the value you bring to Epic.

2.7 Average Timeline

The Epic Business Intelligence interview process generally spans 3-5 weeks from initial application to final offer. Fast-track candidates with highly relevant experience and strong technical skills may complete the process in as little as 2-3 weeks, while the standard pace allows for a week or more between stages to accommodate interview scheduling and project assignments. Take-home case studies or technical assessments typically have a 3-5 day completion window, and onsite rounds are scheduled based on team availability.

Next, let’s dive into the specific interview questions you’re likely to encounter at each stage.

3.1 Data Analysis & SQL

Business Intelligence roles at Epic require strong analytical skills, fluency in SQL, and the ability to extract actionable insights from complex datasets. Expect questions that test your ability to write queries, aggregate data, and perform advanced analyses that drive business decisions.

3.1.1 Write a SQL query to count transactions filtered by several criterias.
Explain how to use SQL filtering, grouping, and aggregation to count transactions based on specific business logic. Be clear about handling edge cases and null values.

3.1.2 Write a query to calculate the conversion rate for each trial experiment variant
Show how to aggregate user data by experiment variant, count conversions, and compute conversion rates. Mention how you would handle missing or incomplete data.

3.1.3 *We're interested in how user activity affects user purchasing behavior. *
Describe how you would join datasets, define key activity metrics, and analyze their relationship to purchasing events. Discuss approaches for measuring correlation or causality.

3.1.4 You’re tasked with analyzing data from multiple sources, such as payment transactions, user behavior, and fraud detection logs. How would you approach solving a data analytics problem involving these diverse datasets? What steps would you take to clean, combine, and extract meaningful insights that could improve the system's performance?
Outline a step-by-step process for data cleaning, joining, and reconciling different data sources. Emphasize data validation and the importance of aligning business definitions.

3.2 Experimentation & Metrics

Measuring success through experiments and KPIs is core to BI. You’ll be expected to design, analyze, and interpret experiments, and to select metrics that align with business goals.

3.2.1 The role of A/B testing in measuring the success rate of an analytics experiment
Discuss experimental design, the importance of control groups, and how to measure uplift and statistical significance. Highlight how you’d communicate findings to stakeholders.

3.2.2 How would you establish causal inference to measure the effect of curated playlists on engagement without A/B?
Describe methods such as propensity score matching or difference-in-differences. Explain how you’d control for confounders and validate your conclusions.

3.2.3 Let's say you work at Facebook and you're analyzing churn on the platform.
Explain how to define and calculate retention metrics, segment users, and analyze factors affecting churn. Address how you’d handle data limitations.

3.2.4 How to model merchant acquisition in a new market?
Detail your approach to defining acquisition metrics, modeling growth, and identifying leading indicators of success. Include how you’d use historical or proxy data.

3.3 Data Engineering & Pipeline Design

Epic BI professionals are often responsible for designing scalable data pipelines and ensuring data quality. You should be ready to discuss ETL, warehousing, and monitoring.

3.3.1 Design a data warehouse for a new online retailer
Describe your process for schema design, normalization vs. denormalization, and how you’d enable efficient querying and reporting.

3.3.2 Let's say that you're in charge of getting payment data into your internal data warehouse.
Explain how you’d design the ETL pipeline, manage data quality, and monitor for failures. Discuss strategies for handling late-arriving or malformed data.

3.3.3 Design a data pipeline for hourly user analytics.
Outline your approach to ingesting, aggregating, and serving data at scale. Mention considerations for latency, reliability, and cost.

3.3.4 Ensuring data quality within a complex ETL setup
Discuss practices for automated validation, anomaly detection, and data lineage. Emphasize collaboration with stakeholders to define quality benchmarks.

3.4 Dashboarding, Visualization & Communication

Communicating insights to diverse audiences is critical. You’ll be asked about dashboard design, data storytelling, and making analytics accessible to non-technical users.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Describe your approach to tailoring presentations, choosing the right visualizations, and adjusting technical depth based on audience needs.

3.4.2 Demystifying data for non-technical users through visualization and clear communication
Explain how you use visual storytelling, analogies, and interactive dashboards to make data more approachable.

3.4.3 Making data-driven insights actionable for those without technical expertise
Share how you translate analytics into clear recommendations, using examples or narratives that resonate with business users.

3.4.4 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.
Discuss your process for requirements gathering, selecting key metrics, and designing intuitive, actionable dashboards.

3.5 Product & Business Case Analysis

Business Intelligence at Epic often involves evaluating the impact of features, promotions, and operational changes. Prepare for questions that test your ability to structure and assess business problems quantitatively.

3.5.1 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?
Explain how you’d set up an experiment, define success metrics (e.g., incremental revenue, retention), and analyze both short-term and long-term effects.

3.5.2 How would you analyze how the feature is performing?
Describe your approach to defining KPIs, segmenting users, and measuring impact over time. Include how you’d communicate findings and recommend next steps.

3.5.3 What kind of analysis would you conduct to recommend changes to the UI?
Discuss how you’d combine quantitative funnel analysis with qualitative feedback, and how you’d prioritize recommendations based on business objectives.

3.5.4 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 to align messages, calculate response times, and aggregate by user for actionable insights.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Focus on a specific instance where your analysis directly influenced a business outcome. Highlight how you identified the opportunity, performed the analysis, and communicated your recommendation.

3.6.2 Describe a challenging data project and how you handled it.
Choose a project with technical or organizational hurdles. Emphasize your problem-solving process, stakeholder management, and the impact of your solution.

3.6.3 How do you handle unclear requirements or ambiguity?
Explain your approach to clarifying objectives, asking probing questions, and iterating with stakeholders. Give an example where you successfully delivered results despite initial 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?
Discuss how you facilitated open dialogue, incorporated feedback, and built consensus. Highlight the importance of collaboration and adaptability.

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?
Share how you quantified additional work, communicated trade-offs, and used prioritization frameworks to maintain focus and data quality.

3.6.6 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a dashboard quickly.
Describe your decision process for prioritizing essential features, documenting caveats, and planning for future improvements.

3.6.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Explain how you built trust, presented evidence, and navigated organizational dynamics to drive adoption.

3.6.8 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Describe your prototyping process, how you gathered feedback, and how this approach accelerated alignment and delivery.

3.6.9 Tell me about a project where you had to make a tradeoff between speed and accuracy.
Discuss the factors you considered, how you communicated risks, and the impact of your decision on the project outcome.

3.6.10 Tell us about a time you caught an error in your analysis after sharing results. What did you do next?
Be honest about the mistake, detail your corrective actions, and emphasize transparency and continuous improvement.

4.1 Company-specific tips:

Demonstrate a deep understanding of Epic’s mission to improve healthcare outcomes through technology. Research how Epic’s software solutions impact patient care, hospital operations, and healthcare research, and be ready to discuss how data-driven insights can support these goals.

Familiarize yourself with the unique challenges of healthcare data, such as privacy regulations (HIPAA), interoperability, and the complexity of electronic health records. Show that you appreciate the importance of data security and accuracy within a healthcare setting.

Review Epic’s suite of products and recent initiatives. Be prepared to discuss how business intelligence can drive improvements in patient safety, operational efficiency, and clinical decision-making. Relate your experience to Epic’s context, such as supporting hospitals, clinics, or population health programs.

Highlight your ability to communicate complex technical findings to both technical and non-technical stakeholders. Epic values professionals who can bridge the gap between analytics and action, so prepare examples where your insights led to meaningful business or clinical outcomes.

4.2 Role-specific tips:

Showcase your expertise in SQL by preparing to write queries that handle healthcare data scenarios, such as aggregating patient records, analyzing user activity, or tracking conversion rates for clinical trials. Practice explaining your approach to filtering, joining, and aggregating data, especially when handling missing or inconsistent values.

Demonstrate your experience with data modeling and ETL pipeline design. Be ready to discuss how you would architect scalable data solutions for diverse sources like payment transactions, user logs, and clinical data. Focus on your process for data cleaning, validation, and ensuring data quality across complex pipelines.

Prepare to discuss your approach to experiment analysis, including A/B testing and causal inference. Explain how you would design experiments to measure the impact of new features or process changes, select appropriate success metrics, and interpret statistical significance. If asked about situations where A/B testing isn’t possible, describe alternative methods such as propensity score matching or difference-in-differences.

Practice presenting data insights through dashboards and visualizations. Be ready to walk through your process for designing dashboards tailored to different audiences, from clinicians to executives. Explain how you select key metrics, choose effective visualizations, and make data actionable for users with varying technical backgrounds.

Emphasize your experience working with stakeholders to define business requirements, prioritize data projects, and align on key performance indicators. Prepare stories that highlight your ability to navigate ambiguity, clarify objectives, and deliver solutions that drive business value.

Reflect on your behavioral and communication skills. Think of examples where you managed project scope, resolved disagreements, or influenced stakeholders without formal authority. Show that you are adaptable, collaborative, and capable of driving consensus in cross-functional teams.

Be prepared to discuss tradeoffs between speed and accuracy, especially in high-stakes healthcare environments. Articulate how you balance the need for timely insights with the responsibility to ensure data integrity and reliability.

Finally, anticipate questions about learning from mistakes. Have a story ready where you caught an error in your analysis, took corrective action, and demonstrated transparency and a commitment to continuous improvement. This will show your integrity and dedication to quality—traits highly valued at Epic.

5.1 How hard is the Epic Business Intelligence interview?
The Epic Business Intelligence interview is challenging, especially for those new to healthcare data or BI roles. You’ll be tested on your technical skills in SQL, data modeling, dashboard design, and experiment analysis, as well as your ability to communicate complex insights to both technical and non-technical stakeholders. Epic places a strong emphasis on real-world problem solving and expects candidates to demonstrate a clear understanding of healthcare data complexities, privacy, and the impact of analytics on patient care. Candidates who prepare thoroughly and can showcase their ability to drive actionable insights from complex datasets will find the process rewarding.

5.2 How many interview rounds does Epic have for Business Intelligence?
Epic typically has 5-6 interview rounds for Business Intelligence roles. The process starts with an application and resume review, followed by a recruiter screen, technical/case interviews, a behavioral interview, and a final onsite round with multiple team members. Each stage is designed to assess both your technical expertise and your ability to collaborate and communicate within a dynamic, cross-functional environment.

5.3 Does Epic ask for take-home assignments for Business Intelligence?
Yes, Epic often includes take-home case studies or technical assessments as part of the Business Intelligence interview process. These assignments usually focus on real-world scenarios such as data cleaning, analysis, or dashboard design, and allow you to demonstrate your approach to solving complex business problems using data. You’ll typically have several days to complete these assignments and present your findings.

5.4 What skills are required for the Epic Business Intelligence?
Key skills for the Epic Business Intelligence role include advanced SQL proficiency, data modeling, ETL pipeline design, dashboard creation, data visualization, and experiment analysis (A/B testing, causal inference). Strong communication and stakeholder management skills are essential, as you’ll often need to present findings to non-technical audiences and drive alignment across teams. Familiarity with healthcare data privacy and regulatory requirements (such as HIPAA) is highly valued, along with the ability to deliver actionable insights that support Epic’s mission to improve healthcare outcomes.

5.5 How long does the Epic Business Intelligence hiring process take?
The Epic Business Intelligence hiring process usually takes 3-5 weeks from initial application to final offer. Timelines can vary based on candidate availability and scheduling logistics, but most candidates can expect a week or more between interview stages. Take-home assignments typically have a 3-5 day completion window, and onsite interviews are scheduled according to team availability.

5.6 What types of questions are asked in the Epic Business Intelligence interview?
Expect a mix of technical, case-based, and behavioral questions. Technical questions cover SQL queries, data modeling, ETL pipeline design, and dashboard creation. Case studies may involve analyzing healthcare data, designing experiments, or recommending business strategies based on analytics. Behavioral questions focus on stakeholder communication, handling ambiguity, project management, and learning from mistakes. You’ll also be asked to present data insights and explain your decision-making process to both technical and non-technical audiences.

5.7 Does Epic give feedback after the Business Intelligence interview?
Epic typically provides high-level feedback through recruiters after the interview process. While detailed technical feedback may be limited, you will usually receive information about your overall performance and fit for the role. Epic values transparency and professionalism, so don’t hesitate to ask your recruiter for additional insights if you’re looking to improve for future interviews.

5.8 What is the acceptance rate for Epic Business Intelligence applicants?
Epic Business Intelligence roles are highly competitive, with an estimated acceptance rate of 3-5% for qualified applicants. Epic looks for candidates with a strong technical foundation, healthcare data awareness, and excellent communication skills. Standing out requires a well-tailored resume, thorough interview preparation, and the ability to demonstrate impact through data-driven decision making.

5.9 Does Epic hire remote Business Intelligence positions?
Epic has traditionally emphasized onsite collaboration at its headquarters in Verona, Wisconsin, but remote opportunities are becoming more common, especially for Business Intelligence roles supporting distributed teams and clients. Some positions may require occasional travel for team meetings or client engagements, so clarify remote work expectations with your recruiter during the interview process.

• Related guides:
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  • 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 Epic Business Intelligence interview? It’s not just about knowing the technical skills—you need to think like an Epic 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 Epic and similar companies.

With resources like the Epic 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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