Butterfly network Data Analyst Interview Guide

Getting ready for a Data Analyst interview at Butterfly Network? The Butterfly Network Data Analyst interview process typically spans technical, analytical, and business-focused question topics, evaluating skills in areas like data pipeline design, data cleaning and organization, stakeholder communication, and presenting actionable insights. Interview preparation is especially important for this role at Butterfly Network, as analysts are expected to transform diverse healthcare and operational data into clear, impactful recommendations that drive product innovation and improve patient outcomes in a rapidly evolving medical technology environment.

In preparing for the interview, you should:

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

Butterfly Network is a pioneering medical technology company that develops and manufactures handheld, AI-powered ultrasound devices designed to make medical imaging more accessible and affordable worldwide. By combining advanced ultrasound technology with cloud-based software, Butterfly enables clinicians to perform diagnostic imaging at the point of care, improving patient outcomes and streamlining workflows. As a Data Analyst at Butterfly Network, you will contribute to harnessing healthcare data and analytics to optimize product performance, inform clinical insights, and support the company's mission of democratizing medical imaging globally.

As a Data Analyst at Butterfly Network, you will be responsible for gathering, processing, and interpreting healthcare and device-related data to support data-driven decision-making across the organization. You will collaborate with product, engineering, and clinical teams to develop reports, build dashboards, and uncover insights that inform product enhancements and business strategies. Typical duties include analyzing usage trends, evaluating operational metrics, and presenting findings to stakeholders to drive improvements in Butterfly Network’s medical imaging solutions. This role is essential in helping the company advance its mission to make medical imaging more accessible and efficient through innovative technology.

2.1 Stage 1: Application & Resume Review

The process begins with a detailed review of your application and resume, where the recruiting team evaluates your background for alignment with the data analyst role. They look for experience in data analysis, SQL, Python, data visualization, ETL pipeline design, and the ability to communicate insights clearly to both technical and non-technical stakeholders. Highlighting experience with data cleaning, pipeline development, and business impact is key at this stage. To prepare, ensure your resume succinctly showcases quantifiable achievements, relevant technical skills, and your ability to drive actionable insights.

2.2 Stage 2: Recruiter Screen

Next is a recruiter screen, typically a 30-minute virtual conversation with a member of the talent acquisition team. The recruiter will discuss your interest in Butterfly Network, your understanding of the company’s mission, and your motivation for applying. They may briefly touch on your experience with data analytics tools, communication with stakeholders, and handling data quality issues. Preparation should include a concise career narrative, clear reasons for your interest in the company, and readiness to discuss your most relevant skills and experiences.

2.3 Stage 3: Technical/Case/Skills Round

The technical round is usually conducted virtually and may involve one or more interviews with data team members or a hiring manager. Expect a mix of SQL and Python coding challenges, case studies involving real-world data projects, and scenario-based questions on designing ETL pipelines, data cleaning, and analytics for business outcomes. You may be asked to interpret data trends, design scalable solutions, or explain your approach to integrating multiple data sources. To prepare, review your hands-on experience, practice articulating your problem-solving methodology, and be ready to discuss technical trade-offs and decisions.

2.4 Stage 4: Behavioral Interview

The behavioral interview focuses on your interpersonal skills, adaptability, and ability to communicate complex data insights to a variety of audiences. Interviewers will assess your experience collaborating with cross-functional teams, resolving stakeholder misalignment, and making data accessible for non-technical users. Be prepared to share specific examples where you addressed project challenges, improved data quality, or drove business impact through analytics. Use the STAR (Situation, Task, Action, Result) method to structure your responses for clarity and impact.

2.5 Stage 5: Final/Onsite Round

The final stage often consists of a multi-part onsite or virtual onsite interview, involving several team members from analytics, engineering, and business functions. This round may include a mix of technical deep-dives, case discussions, and presentations where you’ll be asked to walk through a data project or present insights to a non-technical audience. You may also be evaluated on your approach to stakeholder communication, designing dashboards, and handling ambiguous business problems. Preparation should focus on synthesizing complex information, tailoring your communication to the audience, and demonstrating a collaborative mindset.

2.6 Stage 6: Offer & Negotiation

If you successfully progress through the previous stages, the recruiter will reach out with an offer and initiate the negotiation process. This conversation covers compensation, benefits, team placement, and start date. Be prepared to discuss your expectations, clarify any questions about the role, and negotiate terms that align with your goals.

2.7 Average Timeline

The typical Butterfly Network Data Analyst interview process spans about 3-4 weeks from application to offer, with each stage generally taking about a week. Fast-track candidates with highly relevant experience may move through the process in as little as 2 weeks, while standard timelines may be extended depending on interviewer availability and scheduling logistics. Communication between rounds can occasionally be delayed, so proactive follow-up is recommended.

Next, let’s dive into the specific types of interview questions you can expect at each stage of the Butterfly Network Data Analyst process.

3.1 Data Cleaning & Quality

For Butterfly Network, ensuring high data integrity is essential for reliable analytics, especially when working with healthcare imaging and device data. Expect questions that probe your ability to identify, resolve, and communicate data quality issues across diverse and complex datasets.

3.1.1 Describing a real-world data cleaning and organization project
Discuss your step-by-step approach to profiling, cleaning, and validating messy datasets. Emphasize techniques for handling missing values, duplicates, and inconsistent formats, and how you documented your process for auditability.

Example answer: “I started by profiling the data with summary statistics and visualizations to pinpoint outliers and missingness. I designed a cleaning workflow that used both automated scripts and manual review for edge cases, then validated results with reproducible notebooks and shared confidence intervals with stakeholders.”

3.1.2 How would you approach improving the quality of airline data?
Outline your strategy for diagnosing root causes of poor data quality, prioritizing fixes by impact, and implementing automated checks to prevent future issues. Highlight your communication with cross-functional teams to ensure alignment on standards.

Example answer: “I would conduct an initial audit for completeness, accuracy, and consistency, then collaborate with engineering to set up automated validation rules. I’d prioritize fixes that impact business-critical metrics and report progress with transparent dashboards.”

3.1.3 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?
Demonstrate your method for standardizing schemas, resolving key mismatches, and merging datasets while maintaining traceability. Stress your ability to identify and address data quality gaps before analysis.

Example answer: “I’d begin by profiling each source for schema compatibility and data quality, then use common keys to join datasets. I’d document all transformation steps and apply targeted cleaning to high-impact fields, allowing for robust cross-source insights.”

3.1.4 Redesign batch ingestion to real-time streaming for financial transactions.
Explain how you’d transition from batch to streaming data pipelines, focusing on reliability, latency, and error handling. Discuss trade-offs between speed and data integrity in a production environment.

Example answer: “I’d architect a streaming pipeline using event-driven tools, with checkpoints for error recovery and automated alerts for anomalies. I’d validate real-time data integrity through continuous monitoring and fallback batch jobs.”

3.2 Data Analysis & Experimentation

Butterfly Network values analysts who can design robust experiments, interpret results, and translate data into actionable business insights. Prepare to discuss methodologies for A/B testing, cohort analysis, and measuring success in product or operational initiatives.

3.2.1 The role of A/B testing in measuring the success rate of an analytics experiment
Describe how you design experiments, select appropriate control groups, and define success metrics. Emphasize statistical rigor and clear communication of results.

Example answer: “I set up randomized control and treatment groups, pre-registered hypotheses, and tracked key metrics. I used statistical tests to measure lift and communicated findings with confidence intervals to stakeholders.”

3.2.2 We're interested in determining if a data scientist who switches jobs more often ends up getting promoted to a manager role faster than a data scientist that stays at one job for longer.
Explain your approach to cohort analysis and regression modeling to uncover trends in promotion timelines. Highlight how you control for confounding variables.

Example answer: “I’d segment data scientists into cohorts by job tenure and use survival analysis to compare promotion rates, controlling for education and company size.”

3.2.3 Assessing the market potential and then use A/B testing to measure its effectiveness against user behavior
Detail how you’d size the market, design experiments, and analyze user engagement metrics to inform product decisions.

Example answer: “I’d estimate market size using external and internal data, then run A/B tests on new features and measure changes in user engagement and conversion rates.”

3.2.4 How would you approach sizing the market, segmenting users, identifying competitors, and building a marketing plan for a new smart fitness tracker?
Break down your approach to market research, user segmentation, and competitive analysis. Discuss how you’d use data to inform marketing strategy.

Example answer: “I’d analyze industry reports and user data to size the market, segment users by demographics and usage patterns, and benchmark competitors’ features to shape our marketing plan.”

3.3 Data Engineering & Pipeline Design

As a Data Analyst at Butterfly Network, you’ll often work closely with engineering to design scalable data pipelines and optimize storage for large imaging and device datasets. Expect questions that assess your technical understanding of ETL, warehousing, and real-time analytics.

3.3.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Share your process for building flexible, automated ETL workflows that handle diverse data formats and volumes. Mention monitoring, error handling, and scalability.

Example answer: “I’d use modular ETL components that validate and transform data on ingestion, with automated logging and alerts for failures. I’d optimize for scalability by batching and parallelizing jobs.”

3.3.2 Design a data warehouse for a new online retailer
Discuss your approach to schema design, normalization, and indexing for efficient querying and reporting. Highlight considerations for future scalability.

Example answer: “I’d design star or snowflake schemas to balance query performance and flexibility, with indexed fact and dimension tables for common analytics queries.”

3.3.3 Migrating a social network's data from a document database to a relational database for better data metrics
Explain how you’d plan and execute a data migration, including mapping schemas, handling data integrity, and minimizing downtime.

Example answer: “I’d map document fields to relational tables, use ETL tools to migrate data in phases, and validate results with pre- and post-migration checks.”

3.3.4 Design a data pipeline for hourly user analytics.
Describe how you’d aggregate user data on an hourly basis, ensuring efficiency, reliability, and timely reporting.

Example answer: “I’d schedule hourly ETL jobs with incremental updates and use partitioned tables to optimize query speed for time-based analytics.”

3.4 Stakeholder Communication & Data Visualization

Butterfly Network requires analysts who can translate complex analytics into clear, actionable insights for technical and non-technical audiences. You’ll be expected to present findings, tailor messages, and drive alignment across teams.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Explain your approach to customizing presentations for different stakeholder groups, using visualizations and storytelling to maximize impact.

Example answer: “I tailor my presentations by using simple visuals and focusing on key takeaways. I adjust technical depth based on the audience and provide actionable recommendations.”

3.4.2 Making data-driven insights actionable for those without technical expertise
Discuss strategies for simplifying technical findings, using analogies, and focusing on business relevance.

Example answer: “I translate findings into plain language, use analogies to explain complex concepts, and link recommendations directly to business outcomes.”

3.4.3 Demystifying data for non-technical users through visualization and clear communication
Highlight your use of dashboards, infographics, and interactive tools to make data accessible.

Example answer: “I build interactive dashboards with intuitive filters and concise annotations, enabling non-technical users to explore data independently.”

3.4.4 Strategically resolving misaligned expectations with stakeholders for a successful project outcome
Describe your process for aligning stakeholders, managing expectations, and communicating project scope and trade-offs.

Example answer: “I hold regular syncs to clarify goals, document changes in a shared log, and communicate the impact of new requests on timelines and data quality.”

3.5 SQL & Querying Skills

Strong SQL skills are fundamental for Butterfly Network Data Analysts, especially when querying large device logs and user data. You’ll be tested on your ability to write efficient queries and interpret results accurately.

3.5.1 Write a query to compute the average time it takes for each user to respond to the previous system message
Demonstrate your ability to use window functions and join logic to align messages and calculate response times.

Example answer: “I’d use window functions to pair each user message with the preceding system message, then calculate and average the response intervals by user.”

3.5.2 Write a query to find all users that were at some point "Excited" and have never been "Bored" with a campaign
Show how you use conditional aggregation or filtering to identify users meeting both criteria efficiently.

Example answer: “I’d group by user, filter for those with ‘Excited’ events and exclude those with any ‘Bored’ events using HAVING clauses.”

3.5.3 Modifying a billion rows
Explain your approach to updating or transforming massive tables efficiently, considering indexing, batching, and minimizing lock contention.

Example answer: “I’d batch updates in manageable chunks, leverage partitioning, and schedule jobs during off-peak hours to reduce impact.”

3.5.4 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Describe the steps from raw data ingestion to model deployment and reporting, focusing on reliability and scalability.

Example answer: “I’d design ETL processes to clean and aggregate rental data, train predictive models, and serve results via dashboards and APIs.”

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Describe how you identified the problem, analyzed the data, and influenced a business outcome. Highlight measurable impact and communication with stakeholders.

3.6.2 Describe a challenging data project and how you handled it.
Explain the obstacles, your approach to overcoming them, and the final result. Emphasize adaptability, collaboration, and lessons learned.

3.6.3 How do you handle unclear requirements or ambiguity?
Share your strategy for clarifying goals, iterating on deliverables, and keeping stakeholders informed throughout the process.

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, presented evidence, and reached consensus or 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 impact, communicated trade-offs, and used prioritization frameworks to maintain project integrity.

3.6.6 When leadership demanded a quicker deadline than you felt was realistic, what steps did you take to reset expectations while still showing progress?
Detail how you communicated risks, broke down deliverables, and delivered interim results to maintain trust.

3.6.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Show your use of storytelling, data prototypes, and stakeholder engagement to drive alignment and action.

3.6.8 Describe how you prioritized backlog items when multiple executives marked their requests as “high priority.”
Share your prioritization framework, communication strategy, and how you balanced competing needs.

3.6.9 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Explain your approach to building reusable scripts, integrating checks into pipelines, and sharing results with the team.

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?
Describe your missingness analysis, chosen imputation or exclusion strategy, and how you communicated uncertainty to decision-makers.

4.1 Company-specific tips:

Get familiar with Butterfly Network’s mission to democratize medical imaging through AI-powered handheld ultrasound devices. Understand how data analytics play a direct role in improving patient outcomes, streamlining clinical workflows, and driving product innovation in the healthcare technology space. Review recent product launches, partnerships, and the company’s approach to integrating cloud-based software with medical devices. Be ready to discuss how data-driven insights can impact clinical operations, device adoption, and healthcare accessibility.

Research the regulatory and compliance landscape surrounding healthcare data, including HIPAA, data privacy, and security requirements. Butterfly Network works with sensitive patient information and device-generated data, so demonstrating awareness of best practices in handling, storing, and analyzing healthcare datasets will set you apart.

Learn about the unique challenges of working with medical imaging data, such as variability in data formats, the importance of data quality for diagnostic accuracy, and the role of AI in enhancing image interpretation. Understanding these aspects will help you contextualize your analytical skills within Butterfly Network’s environment.

4.2 Role-specific tips:

4.2.1 Practice designing and explaining ETL pipelines for heterogeneous healthcare data.
Showcase your ability to build scalable ETL workflows that can ingest, clean, and transform diverse datasets from devices, clinical systems, and cloud platforms. Be prepared to discuss pipeline reliability, error handling, and how you’d automate data validation to maintain high data integrity—critical for clinical decision support and product analytics.

4.2.2 Demonstrate expertise in data cleaning and quality assurance for medical and device data.
Highlight your experience identifying and resolving data quality issues, such as missing values, duplicates, and inconsistent formats, especially in complex healthcare datasets. Explain your process for profiling data, documenting cleaning steps, and ensuring reproducibility for audits and compliance.

4.2.3 Prepare to analyze and interpret healthcare usage trends, operational metrics, and clinical outcomes.
Develop examples of how you’ve analyzed user engagement, device utilization, or clinical workflow data to uncover actionable insights. Discuss your approach to segmenting users (e.g., clinicians, departments), tracking adoption patterns, and measuring the impact of product updates on patient care.

4.2.4 Strengthen your SQL skills with queries focused on time-series data, device logs, and user behavior analytics.
Practice writing efficient SQL queries that aggregate, filter, and join large volumes of device and clinical data. Be ready to use window functions, conditional logic, and advanced joins to answer business-critical questions about system usage, response times, and operational anomalies.

4.2.5 Prepare to communicate complex data insights to both technical and non-technical stakeholders.
Develop clear, concise ways to present your findings using dashboards, visualizations, and business storytelling. Focus on tailoring your message to different audiences—clinicians, engineers, and executives—highlighting the impact of your analysis on patient outcomes, operational efficiency, and strategic decision-making.

4.2.6 Review statistical concepts relevant to healthcare analytics, including cohort analysis, A/B testing, and outcome measurement.
Be ready to design experiments that measure the effectiveness of product features or clinical interventions. Discuss how you select control groups, define success metrics, and interpret results in the context of healthcare improvement.

4.2.7 Prepare examples of collaborating with cross-functional teams to deliver data-driven recommendations.
Share stories of how you partnered with product, engineering, or clinical teams to solve problems, align on project goals, and turn analytics into actionable strategies. Emphasize your adaptability, communication skills, and ability to influence decision-making without formal authority.

4.2.8 Practice handling ambiguous requirements and prioritizing competing stakeholder requests.
Demonstrate your approach to clarifying business objectives, negotiating scope, and using prioritization frameworks to balance multiple high-priority tasks. Show how you keep projects on track while ensuring the most impactful analytics are delivered.

4.2.9 Be ready to discuss automating data-quality checks and building reusable analytics workflows.
Highlight your experience developing scripts or tools that continuously monitor data integrity, prevent recurring issues, and improve the reliability of reporting and insights. Explain how you share these improvements with your team to drive long-term value.

4.2.10 Prepare to address analytical trade-offs when working with incomplete or messy healthcare datasets.
Discuss your strategies for handling missing data, choosing between imputation and exclusion, and communicating uncertainty to decision-makers. Show your ability to deliver critical insights even when data is imperfect, balancing rigor with practical business impact.

5.1 How hard is the Butterfly Network Data Analyst interview?
The Butterfly Network Data Analyst interview is considered moderately challenging, with a strong emphasis on technical analytics, healthcare data problem-solving, and stakeholder communication. Candidates are expected to demonstrate expertise in designing and cleaning data pipelines, interpreting healthcare metrics, and presenting actionable insights that drive product innovation and improve patient outcomes. Familiarity with medical technology and an ability to translate complex data into clear business recommendations will give you a distinct advantage.

5.2 How many interview rounds does Butterfly Network have for Data Analyst?
Typically, the Butterfly Network Data Analyst interview process consists of 5-6 rounds. This includes an initial resume review, a recruiter screen, one or more technical/case/skills interviews, a behavioral interview, and a final onsite (or virtual onsite) round. Each stage is designed to assess different facets of your analytical, technical, and communication skills.

5.3 Does Butterfly Network ask for take-home assignments for Data Analyst?
While take-home assignments are not always guaranteed, Butterfly Network occasionally includes a practical exercise or case study as part of the technical or final round. These assignments may involve analyzing healthcare datasets, designing ETL pipelines, or presenting insights in a format tailored to non-technical stakeholders. The goal is to evaluate your real-world problem-solving and communication abilities.

5.4 What skills are required for the Butterfly Network Data Analyst?
Key skills for the Butterfly Network Data Analyst include advanced SQL querying, Python programming, ETL pipeline design, data cleaning and quality assurance, data visualization, and stakeholder communication. Familiarity with healthcare data standards, cloud-based analytics, and statistical experimentation (such as A/B testing and cohort analysis) are highly valued. The ability to synthesize complex information and deliver actionable recommendations to both technical and clinical teams is essential.

5.5 How long does the Butterfly Network Data Analyst hiring process take?
The typical timeline for the Butterfly Network Data Analyst hiring process is 3-4 weeks from application to offer. Each stage generally takes about a week, though fast-track candidates may move through in as little as 2 weeks. Timelines can be extended based on interviewer availability, scheduling logistics, and candidate responsiveness.

5.6 What types of questions are asked in the Butterfly Network Data Analyst interview?
Expect a mix of technical analytics questions (SQL, Python, ETL pipeline design), healthcare data case studies, stakeholder communication scenarios, and behavioral questions. You may be asked to clean and analyze messy datasets, design scalable data solutions, interpret healthcare usage trends, and present findings to both technical and non-technical audiences. Behavioral questions often focus on collaboration, adaptability, and driving business impact through data.

5.7 Does Butterfly Network give feedback after the Data Analyst interview?
Butterfly Network typically provides high-level feedback through recruiters, especially regarding your fit for the role and general performance. Detailed technical feedback may be limited, but you can expect constructive insights on your strengths and areas for improvement if you progress through multiple rounds.

5.8 What is the acceptance rate for Butterfly Network Data Analyst applicants?
While specific acceptance rates are not publicly disclosed, the Data Analyst role at Butterfly Network is competitive, with an estimated acceptance rate of around 3-5% for qualified applicants. Strong healthcare analytics experience and the ability to communicate insights effectively will help you stand out.

5.9 Does Butterfly Network hire remote Data Analyst positions?
Yes, Butterfly Network does offer remote Data Analyst roles, though some positions may require periodic onsite visits for team collaboration or project work, especially when interfacing with clinical or engineering teams. Flexibility in work location is increasingly supported, reflecting the company’s commitment to attracting top analytics talent.

• Related guides:
  • Butterfly Network interview questions
  • Data Analyst interview guide
  • Top data analyst interview tips

Ready to ace your Butterfly Network Data Analyst interview? It’s not just about knowing the technical skills—you need to think like a Butterfly Network Data Analyst, solve problems under pressure, and connect your expertise to real business impact in healthcare technology. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at Butterfly Network and similar companies.

With resources like the Butterfly Network Data Analyst 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 deep into topics like healthcare data cleaning, ETL pipeline design, SQL for device logs, stakeholder communication, and presenting actionable insights that drive innovation in medical imaging.

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!