Lyra Health Data Analyst Interview Guide

Getting ready for a Data Analyst interview at Lyra Health? The Lyra Health Data Analyst interview process typically spans multiple question topics and evaluates skills in areas like SQL, data modeling, data visualization (especially Tableau), analytics, and presenting actionable insights to diverse audiences. Interview prep is especially crucial for this role at Lyra Health, as candidates are expected to translate complex data into clear, impactful recommendations that directly support the company’s mission to improve mental health care through technology and human connection.

In preparing for the interview, you should:

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

Lyra Health is a leading provider of technology-driven mental health care solutions, partnering with major employers to improve access to high-quality behavioral health services for employees and their families. Through its innovative digital care platform and global network of providers, Lyra supports nearly 20 million people in achieving emotional wellness at work and at home. The company emphasizes evidence-based care, rapid access to support, and a holistic approach to well-being. As a Data Analyst, you will contribute to Lyra’s mission by delivering actionable insights and scalable analytics that drive operational excellence and enhance mental health outcomes for clients.

As a Data Analyst at Lyra Health, you will play a key role in supporting customer-facing teams and driving company growth through data-driven insights and strategic recommendations. You will lead large-scale analytics projects, develop scalable data models, and create self-serve reporting solutions to empower stakeholders across the organization. Collaborating closely with data engineering, data science, clinical, and GTM teams, you will define company-wide metrics, analyze business performance, and present actionable findings to both internal and external audiences. This role also involves mentoring junior team members and becoming a subject matter expert in various business areas, ultimately contributing to Lyra Health’s mission of transforming mental health care through technology and data.

2.1 Stage 1: Application & Resume Review

The process begins with a review of your application and resume, where the recruiting team evaluates your background for relevant experience in SQL, analytics, data modeling, and data visualization (with a preference for Tableau). They look for evidence of large-scale analytics projects, dashboard creation, and the ability to communicate insights effectively, as well as experience collaborating cross-functionally and mentoring others. Tailor your resume to highlight specific examples of these skills and quantify your impact where possible.

2.2 Stage 2: Recruiter Screen

Next, you will have an initial call with a recruiter, typically lasting 30–45 minutes. This conversation will focus on your motivation for joining Lyra Health, your experience with SQL, Tableau, and data modeling, and your ability to present complex data clearly. Expect to discuss your approach to analytics, your familiarity with self-serve reporting, and your ability to draw actionable insights. Preparation should include concise stories about past projects, as well as clear articulation of your technical and communication strengths.

2.3 Stage 3: Technical/Case/Skills Round

This stage often consists of one or more technical interviews, which may include a mix of live SQL exercises, case-based analytics questions, and a take-home assignment. You may be asked to write SQL queries (e.g., for rolling averages, engagement rates, or data quality checks), design a Tableau dashboard based on a described business scenario, or develop a scalable data model. The take-home assignment typically involves building a dashboard or analyzing a dataset to draw and present actionable insights. Prepare by practicing advanced SQL, demonstrating your approach to ambiguous data challenges, and showcasing your ability to visualize and communicate data findings effectively.

2.4 Stage 4: Behavioral Interview

A behavioral interview will assess your alignment with Lyra Health’s values and your ability to work in a fast-paced, cross-functional environment. Expect questions about how you’ve mentored junior team members, led analytics projects, managed ambiguity, and communicated insights to non-technical audiences. Use the STAR method (Situation, Task, Action, Result) to structure your responses, and be ready to discuss both successes and challenges in your analytics career.

2.5 Stage 5: Final/Onsite Round

The final stage typically includes a series of interviews with analytics leaders, cross-functional partners, and possibly a panel presentation. You may be asked to present the results of your take-home assignment or walk through a past analytics project, highlighting your technical depth, storytelling ability, and impact. Additional technical or case questions may be presented, and you’ll be evaluated on your ability to synthesize data-driven recommendations for both technical and executive audiences. This stage may involve 2–4 interviews in one day or spread across several days, with each session lasting 30–60 minutes.

2.6 Stage 6: Offer & Negotiation

If successful, you will receive a verbal offer followed by a written offer package. The recruiter will discuss compensation, equity, benefits, and answer any remaining questions about the role or Lyra Health’s culture. This is also your opportunity to negotiate and clarify your responsibilities and growth path within the company.

2.7 Average Timeline

The typical Lyra Health Data Analyst interview process spans 3–5 weeks from initial application to offer, with some candidates moving through more quickly if there is an urgent need or a strong match. Fast-track candidates may complete the process in as little as two weeks, while the standard pace involves a week between each stage to accommodate scheduling and assignment completion. Take-home assignments usually have a 3–5 day deadline, and final rounds are coordinated based on panel availability.

Now that you understand the process, let's dive into the types of interview questions you can expect at each stage.

3.1 SQL & Data Manipulation

Expect questions that assess your ability to query, aggregate, and manipulate large datasets efficiently. These scenarios are designed to test your fluency with SQL and your ability to deliver accurate results under real-world data constraints. Make sure to clarify assumptions and explain your approach to handling edge cases or data quality issues.

3.1.1 Write a query to calculate the 3-day rolling average of steps for each user.
Discuss how to use window functions to partition by user, order by date, and compute rolling aggregates. Mention how you would handle missing days or incomplete data.

3.1.2 Write a query to find the engagement rate for each ad type
Explain how you would join relevant tables, aggregate clicks and impressions, and calculate engagement rates by ad type. Address how to handle nulls or missing engagement data.

3.1.3 Write a query to calculate the conversion rate for each trial experiment variant
Describe grouping trial data by variant, counting conversions, and dividing by total users per group. Note how you would manage incomplete or missing conversion information.

3.1.4 Write a function to return the names and ids for ids that we haven't scraped yet.
Clarify how you would identify missing records using set operations or anti-joins, and return the required fields efficiently.

3.2 Analytics & Metrics

These questions evaluate your ability to define, track, and interpret business and health metrics. You’ll be expected to demonstrate how you turn raw data into actionable insights for product, health, or business decisions.

3.2.1 Create and write queries for health metrics for stack overflow
Discuss how you'd identify key metrics, design queries to monitor platform health, and ensure metrics align with business goals.

3.2.2 Let’s say that you're in charge of an e-commerce D2C business that sells socks. What business health metrics would you care?
Explain which metrics (e.g., conversion rate, retention, average order value) are most important and how you would track them over time.

3.2.3 Which metrics and visualizations would you prioritize for a CEO-facing dashboard during a major rider acquisition campaign?
Describe your approach to choosing high-level, actionable metrics and designing clear visualizations for executive audiences.

3.2.4 How would you approach improving the quality of airline data?
Outline your process for profiling data quality, identifying root causes, and implementing improvements or automated checks.

3.3 Experimentation & Statistical Analysis

These questions focus on your ability to design experiments, interpret results, and apply statistical thinking to real-world problems. Be ready to discuss metrics, hypotheses, and trade-offs.

3.3.1 You work as a data scientist for ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Explain how to set up an A/B test or quasi-experiment, define success metrics, and monitor for unintended consequences.

3.3.2 What kind of analysis would you conduct to recommend changes to the UI?
Discuss how you’d use funnel analysis, cohort analysis, or behavioral segmentation to identify friction points and recommend improvements.

3.3.3 How would you visualize data with long tail text to effectively convey its characteristics and help extract actionable insights?
Describe techniques for summarizing, categorizing, and visualizing text data, such as word clouds, frequency plots, or clustering.

3.3.4 How would you design a data warehouse for a e-commerce company looking to expand internationally?
Explain your approach to schema design, ETL processes, and ensuring scalability for global data needs.

3.4 Data Engineering & Pipeline Design

You may be asked about building, maintaining, and scaling data pipelines. The focus is on your understanding of ETL, data aggregation, and system design for robust analytics.

3.4.1 Design a data pipeline for hourly user analytics.
Describe the ETL steps, technologies, and aggregation strategies you would use for near-real-time analytics.

3.4.2 Let's say that you're in charge of getting payment data into your internal data warehouse.
Detail your approach to data ingestion, validation, and ensuring data integrity throughout the pipeline.

3.4.3 Ensuring data quality within a complex ETL setup
Discuss how you would implement data quality checks, monitoring, and error handling within ETL workflows.

3.4.4 Describe a data project and its challenges
Share how you identified bottlenecks or technical hurdles, and the strategies you used to overcome them.

3.5 Communication & Data Storytelling

These questions assess your ability to communicate complex insights to technical and non-technical audiences, and to make your findings actionable.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Explain your approach to tailoring presentations, simplifying technical language, and using visuals to maximize understanding.

3.5.2 Making data-driven insights actionable for those without technical expertise
Discuss how you translate analytics into business recommendations and ensure stakeholders can act on your insights.

3.5.3 Demystifying data for non-technical users through visualization and clear communication
Describe how you use dashboards, storytelling, and iterative feedback to make data accessible at all levels.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision and how your analysis impacted the business or project outcome.
3.6.2 Describe a challenging data project and how you handled obstacles, including ambiguity or unclear requirements.
3.6.3 Give an example of how you balanced speed versus rigor when leadership needed a “directional” answer by tomorrow.
3.6.4 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
3.6.5 Walk us through how you handled conflicting KPI definitions (e.g., “active user”) between two teams and arrived at a single source of truth.
3.6.6 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
3.6.7 Tell me about a time you delivered critical insights even though a significant portion of the dataset had nulls. What analytical trade-offs did you make?
3.6.8 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
3.6.9 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
3.6.10 Tell me about a time you proactively identified a business opportunity through data.
3.6.11 Describe a time you had to deliver an overnight report and still guarantee the numbers were “executive reliable.” How did you balance speed with data accuracy?

4.1 Company-specific tips:

Familiarize yourself with Lyra Health’s mission and core values, especially its commitment to improving mental health care through technology and evidence-based practices. Understand how Lyra partners with employers to drive better behavioral health outcomes and rapid access to care. Be prepared to discuss how data analytics can support Lyra’s goals of scalability, accessibility, and measurable impact in mental health.

Research Lyra Health’s digital care platform, including its unique approach to matching patients with providers, measuring clinical outcomes, and leveraging data for continuous improvement. Know the major stakeholders you’ll be supporting—such as clinical teams, customer success, and executive leadership—and think about how analytics can empower each group.

Stay up to date on the latest trends in mental health tech, including teletherapy, digital assessments, and data privacy considerations. Demonstrate your understanding of the regulatory landscape and ethical responsibilities when handling sensitive health data.

4.2 Role-specific tips:

4.2.1 Master SQL window functions and aggregation for health data analytics.
Practice writing SQL queries that use window functions to calculate rolling averages, user engagement metrics, and conversion rates—especially in scenarios with missing or incomplete data. Show your ability to handle real-world health datasets, which often require careful partitioning and robust error handling.

4.2.2 Build and present Tableau dashboards tailored for clinical and executive audiences.
Develop sample dashboards that visualize mental health outcomes, engagement trends, or operational metrics, focusing on clarity and actionable insights. Practice tailoring your visualizations for both technical and non-technical stakeholders, such as clinicians and executives, ensuring your findings drive decision-making.

4.2.3 Demonstrate expertise in data modeling and scalable reporting solutions.
Be ready to discuss how you would design scalable data models for tracking patient progress, provider performance, or business KPIs. Share examples of building self-serve reporting tools that empower teams to access insights independently, and describe your process for defining company-wide metrics.

4.2.4 Show how you turn ambiguous, messy data into actionable recommendations.
Prepare stories about tackling incomplete datasets, resolving conflicting KPI definitions, or integrating disparate sources. Highlight your approach to data cleaning, validation, and synthesizing findings into clear recommendations that support Lyra Health’s mission.

4.2.5 Articulate your approach to experimentation and statistical analysis in health tech.
Explain how you design A/B tests, interpret experiment results, and choose appropriate success metrics for behavioral health interventions. Discuss your experience with retention analysis, cohort studies, or measuring the impact of new product features.

4.2.6 Practice communicating complex analytics to cross-functional teams.
Prepare examples of how you’ve presented insights to non-technical audiences, using storytelling and visualization to drive understanding and action. Emphasize your ability to make data accessible, demystify technical jargon, and tailor your message to different stakeholders.

4.2.7 Be ready to discuss mentoring and collaboration in analytics projects.
Share experiences where you’ve mentored junior analysts, led cross-functional analytics initiatives, or influenced stakeholders to adopt data-driven recommendations. Demonstrate leadership, adaptability, and a collaborative spirit aligned with Lyra Health’s values.

4.2.8 Prepare for behavioral questions with clear, structured responses.
Use the STAR method to answer questions about overcoming ambiguity, managing multiple deadlines, handling conflicting data sources, and delivering executive-ready insights under pressure. Practice concise, impactful storytelling that highlights your problem-solving skills and business impact.

5.1 “How hard is the Lyra Health Data Analyst interview?”
The Lyra Health Data Analyst interview is considered moderately challenging, especially for those who have not previously worked in health tech or with sensitive data. The process rigorously tests your SQL skills, ability to build meaningful dashboards (often in Tableau), and your talent for translating complex data into actionable insights for both technical and non-technical stakeholders. The bar is high for communication, analytical rigor, and alignment with Lyra’s mission to improve mental health care. Candidates who can demonstrate both technical depth and a passion for impact in healthcare analytics stand out.

5.2 “How many interview rounds does Lyra Health have for Data Analyst?”
Typically, there are five main stages: application and resume review, recruiter screen, technical/case/skills round (including live SQL and/or take-home analytics assignment), behavioral interview, and a final onsite or virtual panel. The final round often includes a presentation and multiple interviews with analytics leaders and cross-functional partners. You can expect 4–6 interviews in total, with some stages combined depending on scheduling.

5.3 “Does Lyra Health ask for take-home assignments for Data Analyst?”
Yes, most candidates can expect a take-home assignment as part of the technical evaluation. This usually involves analyzing a dataset, building a dashboard (commonly in Tableau), and presenting actionable recommendations. The assignment is designed to test your real-world analytics workflow, data visualization skills, and ability to communicate insights clearly.

5.4 “What skills are required for the Lyra Health Data Analyst?”
Key skills include advanced SQL (especially window functions and data aggregation), strong data modeling, and expertise in data visualization (with a focus on Tableau). The role also demands experience with analytics project leadership, building scalable reporting solutions, and translating ambiguous or messy data into clear business recommendations. Communication skills are critical: you must be able to present insights to both technical and non-technical audiences, often on sensitive mental health topics. Familiarity with experimentation, statistical analysis, and mentoring junior analysts is highly valued.

5.5 “How long does the Lyra Health Data Analyst hiring process take?”
The typical timeline is 3–5 weeks from initial application to offer, though highly aligned candidates may move through in as little as two weeks. Each interview stage is usually spaced a week apart to allow for scheduling and completion of take-home assignments. The process can move faster if there is an urgent need or strong mutual fit.

5.6 “What types of questions are asked in the Lyra Health Data Analyst interview?”
You’ll encounter a mix of technical SQL exercises, analytics case studies, and dashboard-building tasks. Expect questions on health and business metrics, data modeling, and experimentation design. There is a strong emphasis on scenario-based questions that test your ability to handle ambiguous data, present findings to diverse audiences, and make data actionable. Behavioral questions will focus on your experience leading analytics projects, collaborating cross-functionally, and aligning with Lyra Health’s mission and values.

5.7 “Does Lyra Health give feedback after the Data Analyst interview?”
Lyra Health usually provides high-level feedback through the recruiting team, especially after onsite or final rounds. While detailed technical feedback may be limited, you can expect to hear about your general strengths and areas for improvement if you request it.

5.8 “What is the acceptance rate for Lyra Health Data Analyst applicants?”
The process is competitive, reflecting both the technical demands of the role and Lyra Health’s mission-driven culture. While specific acceptance rates are not public, it’s estimated that only a small percentage of applicants reach the offer stage—typically in the range of 3–5% for qualified candidates.

5.9 “Does Lyra Health hire remote Data Analyst positions?”
Yes, Lyra Health offers remote Data Analyst positions, with many roles open to candidates across the United States. Some positions may require occasional travel for team meetings or onsite collaboration, but remote-first and flexible arrangements are common, reflecting Lyra’s commitment to accessibility and work-life balance.

• Related guides:
  • Lyra Health interview questions
  • Data Analyst interview guide
  • Top data analyst interview tips

Ready to ace your Lyra Health Data Analyst interview? It’s not just about knowing the technical skills—you need to think like a Lyra Health Data Analyst, 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 Lyra Health and similar companies.

With resources like the Lyra Health 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. Whether you’re mastering SQL window functions, building Tableau dashboards for clinical and executive audiences, or practicing how to communicate actionable insights in mental health tech, you’ll be ready to showcase your strengths and make a tangible impact.

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!