Surveymonkey Data Analyst Interview Questions + Guide in 2025

Surveymonkey is a global leader in online surveys and forms, empowering individuals and organizations with real-time insights to make informed decisions.

As a Data Analyst at Surveymonkey, you will play a crucial role in enhancing the company's ability to drive sustainable business growth through data-driven insights. Your key responsibilities will include maintaining and supporting analytics processes, ensuring accurate event tracking in collaboration with engineering and product teams, and identifying opportunities for process improvements that streamline workflows. The ideal candidate will bring a strong background in SQL, along with experience in product analytics tools, and possess a detail-oriented mindset to efficiently surface and resolve data inconsistencies. Additionally, a collaborative spirit and the ability to communicate complex analyses clearly to diverse audiences will be essential in fostering a culture of continuous improvement and innovation aligned with Surveymonkey's values of inclusivity and customer-centricity.

This guide will help you prepare for your interview by providing insights into the core responsibilities and skills expected for the Data Analyst role, enabling you to present yourself as a strong candidate.

The interview process for a Data Analyst position at SurveyMonkey is designed to assess both technical skills and cultural fit within the organization. It typically consists of several structured steps that allow candidates to showcase their analytical abilities and problem-solving skills.

1. Initial Recruiter Call

The process begins with a 30-minute phone call with a recruiter. This conversation serves as an introduction to the role and the company, where the recruiter will discuss your background, experience, and motivations for applying. They will also evaluate your fit for the company culture and provide insights into what it’s like to work at SurveyMonkey.

2. Hiring Manager Interview

Following the initial call, candidates will have a one-on-one interview with the hiring manager. This session focuses on your relevant experience and how it aligns with the responsibilities of the Data Analyst role. Expect to discuss your previous projects, particularly those involving SQL and data analysis, as well as your approach to problem-solving and process improvement.

3. Technical Assessment

Candidates will then be required to complete a technical assessment, which may include a SQL test and analytical questions. This step is crucial as it evaluates your technical proficiency and ability to apply analytical skills to real-world scenarios. Be prepared to demonstrate your knowledge of data analysis tools and methodologies.

4. Case Study Presentation

Next, candidates will participate in a case study presentation. This involves analyzing a given dataset and presenting your findings, insights, and recommendations. This step assesses your analytical thinking, communication skills, and ability to convey complex information clearly to both technical and non-technical audiences.

5. Virtual Loop Interviews

Due to the ongoing pandemic, the interview process may include a virtual loop consisting of multiple interviews with various team members. These interviews will cover both technical and behavioral aspects, allowing you to interact with potential colleagues and showcase your collaborative skills.

6. Final Interview with VP

The final step in the process is an interview with a Vice President or senior leader within the organization. This conversation will focus on your long-term career goals, alignment with SurveyMonkey's mission, and how you can contribute to the company's growth and success.

As you prepare for these interviews, it's essential to familiarize yourself with the types of questions that may be asked during each stage.

Here are some tips to help you excel in your interview.

Emphasize Your SQL Proficiency

Given the high importance of SQL in this role, ensure you can demonstrate your expertise. Prepare to discuss your experience with SQL in detail, including specific projects where you utilized SQL for data analysis. Be ready to solve SQL problems on the spot, as interviewers may assess your technical skills through practical tests or case studies. Familiarize yourself with common SQL functions and queries, and practice writing complex queries that involve joins, subqueries, and aggregations.

Prepare for Analytical Questions

Expect analytical questions that test your problem-solving abilities. These may involve case studies or hypothetical scenarios where you need to analyze data and provide insights. Practice structuring your thought process clearly and logically. Use frameworks to approach these questions, such as defining the problem, analyzing the data, and proposing actionable solutions. Remember, the interviewers are looking for your analytical mindset and how you approach data-driven decision-making.

Showcase Your Experience with Product Analytics Tools

Since the role involves working with product analytics tools like Amplitude, be prepared to discuss your familiarity with such platforms. Highlight any relevant experience you have in tracking user behavior, analyzing product metrics, or conducting A/B tests. If you have specific examples of how you used these tools to drive business decisions or improve processes, share those stories to illustrate your impact.

Communicate Clearly and Concisely

Effective communication is crucial in this role, as you will need to convey complex data insights to both technical and non-technical stakeholders. Practice summarizing your analyses in a clear and concise manner. Use visuals or examples to support your points, and be prepared to answer follow-up questions that may require you to elaborate on your findings.

Be Ready for a Collaborative Environment

SurveyMonkey values collaboration across teams, so be prepared to discuss your experience working in cross-functional teams. Share examples of how you have collaborated with product, engineering, or data teams to achieve common goals. Highlight your ability to build relationships and communicate effectively with diverse stakeholders, as this will be key to your success in the role.

Reflect the Company Culture

SurveyMonkey prides itself on being a warm and inclusive workplace. During your interview, embody this culture by being personable and approachable. Show your enthusiasm for the role and the company, and express your alignment with their values. Consider discussing how you can contribute to fostering an inclusive environment and how your unique background can add value to the team.

Prepare for a Multi-Stage Interview Process

The interview process may involve multiple stages, including recruiter calls, technical assessments, and interviews with hiring managers and executives. Approach each stage with the same level of preparation and professionalism. Familiarize yourself with the structure of the interview process and be ready to adapt your responses based on the audience.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Analyst role at SurveyMonkey. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Analyst interview at SurveyMonkey. The interview process will likely focus on your technical skills, particularly in SQL, as well as your analytical thinking and problem-solving abilities. Be prepared to discuss your experience with data analysis, product metrics, and any relevant tools you have used in your previous roles.

SQL and Data Management

1. Can you explain the difference between INNER JOIN and LEFT JOIN in SQL?

Understanding SQL joins is crucial for data analysis, as they allow you to combine data from multiple tables.

How to Answer

Explain the basic definitions of INNER JOIN and LEFT JOIN, and provide a scenario where each would be used.

Example

"An INNER JOIN returns only the rows where there is a match in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table. For example, if I have a table of customers and a table of orders, an INNER JOIN would show only customers who have placed orders, whereas a LEFT JOIN would show all customers, including those who haven't placed any orders."

2. How do you handle missing data in a dataset?

Handling missing data is a common challenge in data analysis.

How to Answer

Discuss various strategies for dealing with missing data, such as imputation, removal, or using algorithms that support missing values.

Example

"I typically assess the extent of missing data and its potential impact on my analysis. If the missing data is minimal, I might remove those records. For larger gaps, I would consider imputation methods, such as using the mean or median for numerical data, or the mode for categorical data, to maintain the dataset's integrity."

3. Describe a complex SQL query you have written. What was its purpose?

This question assesses your practical experience with SQL.

How to Answer

Provide a brief overview of the query, its purpose, and any challenges you faced while writing it.

Example

"I once wrote a complex SQL query to analyze customer purchase patterns over a year. The query involved multiple joins across several tables, aggregating data to show monthly sales trends. One challenge was optimizing the query for performance, which I achieved by indexing key columns."

4. What are window functions in SQL, and when would you use them?

Window functions are essential for performing calculations across a set of table rows related to the current row.

How to Answer

Define window functions and provide an example of a scenario where they would be useful.

Example

"Window functions allow you to perform calculations across a set of rows that are related to the current row. For instance, I used a window function to calculate the running total of sales for each month, which helped in understanding trends over time without losing the context of individual monthly sales."

5. How do you ensure data quality in your analyses?

Data quality is critical for accurate analysis and decision-making.

How to Answer

Discuss the methods you use to validate and clean data before analysis.

Example

"I ensure data quality by implementing a series of validation checks, such as verifying data types, checking for duplicates, and ensuring that there are no outliers that could skew results. Additionally, I often cross-reference data with other reliable sources to confirm its accuracy."

Analytical Thinking and Problem-Solving

1. Describe a time when you identified a significant opportunity for process improvement.

This question evaluates your analytical skills and initiative.

How to Answer

Share a specific example where your analysis led to a positive change in a process.

Example

"In my previous role, I noticed that our reporting process was taking too long due to manual data entry. I proposed automating the data collection using SQL scripts, which reduced the reporting time by 50% and allowed the team to focus on analysis rather than data gathering."

2. How do you prioritize your tasks when working on multiple projects?

Time management is essential in a fast-paced environment.

How to Answer

Explain your approach to prioritization and any tools or methods you use.

Example

"I prioritize tasks based on their deadlines and impact on the business. I often use project management tools like Trello to keep track of my tasks and deadlines. Additionally, I communicate regularly with stakeholders to ensure that I am aligned with their priorities."

3. Can you give an example of how you used data to influence a business decision?

This question assesses your ability to translate data insights into actionable recommendations.

How to Answer

Provide a specific instance where your data analysis led to a decision.

Example

"I analyzed customer feedback data and identified a trend indicating dissatisfaction with a specific product feature. I presented my findings to the product team, which led to a redesign of that feature. Post-implementation, we saw a 30% increase in customer satisfaction scores."

4. What metrics do you consider most important when analyzing product performance?

Understanding key performance indicators (KPIs) is vital for a Data Analyst.

How to Answer

Discuss the metrics you focus on and why they are important.

Example

"I focus on metrics such as user engagement, conversion rates, and customer retention. These metrics provide insights into how well the product is performing and where improvements can be made to enhance user experience and drive growth."

5. How do you approach a new data analysis project?

This question evaluates your project management and analytical skills.

How to Answer

Outline your step-by-step approach to starting a new analysis.

Example

"When starting a new data analysis project, I first define the objectives and key questions to be answered. Next, I gather and clean the relevant data, followed by exploratory data analysis to identify patterns and insights. Finally, I present my findings and recommendations to stakeholders in a clear and actionable format."