Handshake Data Analyst Interview Questions + Guide in 2025

Handshake is a platform dedicated to connecting emerging talent with over 750,000 employers, striving to create an inclusive culture that respects individuals from all backgrounds.

In the role of a Data Analyst at Handshake, you will serve as a vital partner to the Employer Marketing team, leveraging data insights to drive revenue and growth. Your responsibilities will include analyzing marketing campaign performance, developing dashboards and reports, and collaborating with various cross-functional teams to enhance the effectiveness of marketing strategies. A strong command of SQL, familiarity with data visualization tools, and an ability to translate complex data into actionable insights are essential. Your role will require intellectual curiosity, strong analytical skills, and effective communication to advocate for data-driven decision-making across the organization. Candidates who thrive in a fast-paced environment and demonstrate a strategic mindset will excel in this position.

This guide will help you prepare for a job interview by providing insights into the skills and experiences that Handshake values, enabling you to articulate your qualifications and align your responses with the company's mission and objectives.

The interview process for a Data Analyst role at Handshake is structured to assess both technical skills and cultural fit within the team. It typically consists of several key stages:

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, allowing the recruiter to gauge your interest and fit for Handshake's culture. You will discuss your background, relevant experiences, and motivations for applying, as well as any questions you may have about the position.

2. Technical Screening

Following the initial call, candidates will have a technical screening, usually conducted via video call with a member of the data team. This session focuses on your proficiency in SQL and may include questions related to data analysis, statistics, and A/B testing. Be prepared to demonstrate your analytical thinking and problem-solving skills through practical examples.

3. Take-Home Assignment

Candidates who successfully pass the technical screening will receive a take-home assignment. This task involves analyzing a dataset and preparing a presentation that addresses specific guiding questions. The assignment is designed to evaluate your analytical capabilities, attention to detail, and ability to communicate insights effectively.

4. Onsite Interviews

The onsite interview typically consists of multiple rounds, where you will meet with various team members, including product managers and data analysts. During these sessions, you will present your take-home assignment and engage in discussions about data and product strategy. Expect to answer questions that assess your understanding of metrics, reporting, and the impact of marketing campaigns.

5. Final Interviews with Leadership

In the final stage, candidates may meet with senior leadership, such as co-founders or department heads. This round focuses on your strategic thinking, project management skills, and ability to drive cross-functional collaboration. It’s an opportunity for you to demonstrate your alignment with Handshake's mission and values.

As you prepare for the interview process, consider the types of questions that may arise in each of these stages, particularly those related to your analytical skills and experience with data-driven decision-making.

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

Understand the Role's Impact

As a Data Analyst at Handshake, your role is pivotal in driving marketing performance and revenue growth. Familiarize yourself with how your analyses will influence decision-making and strategy. Be prepared to discuss how your previous experiences align with this impact, particularly in a B2B SaaS or marketplace environment. Highlight your ability to translate complex data into actionable insights that can drive business results.

Prepare for Technical Assessments

Given the emphasis on SQL and analytics, ensure you are well-versed in SQL queries, particularly those that involve complex joins, aggregations, and window functions. Brush up on A/B testing methodologies and statistical concepts, as these will likely be focal points during technical interviews. Consider practicing with real datasets to demonstrate your analytical skills and ability to derive insights.

Showcase Your Storytelling Skills

At Handshake, the ability to tell compelling stories with data is crucial. Prepare to present your analyses in a clear and engaging manner. When discussing your take-home assignment or any past projects, focus on how you communicated your findings to stakeholders and the impact those insights had on business decisions. Use visuals effectively in your presentations to enhance understanding.

Emphasize Collaboration and Cross-Functional Work

The role requires collaboration with various teams, including Marketing, Sales, and Revenue Operations. Be ready to discuss your experience working in cross-functional teams and how you’ve driven alignment on projects. Highlight specific examples where your analytical insights led to improved processes or outcomes across departments.

Align with Company Culture

Handshake values diversity and inclusion, so be authentic in your responses and demonstrate your commitment to these principles. Share experiences that reflect your ability to work in diverse teams and how you advocate for inclusivity in your work. This alignment with the company culture will resonate well with your interviewers.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities, adaptability, and project management skills. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Think of specific instances where you faced challenges in your analytical work and how you overcame them, particularly in fast-paced environments.

Be Ready for a Take-Home Assignment

The interview process may include a take-home assignment where you analyze a dataset and present your findings. Approach this task with a strategic mindset, ensuring your analysis is thorough and your presentation is polished. Practice presenting your findings to a friend or colleague to gain confidence and receive feedback.

Engage with the Interviewers

During your interviews, engage with your interviewers by asking insightful questions about the team, projects, and company direction. This not only shows your interest in the role but also helps you gauge if Handshake is the right fit for you. Be curious about their experiences and the challenges they face, as this can provide valuable context for your potential contributions.

By following these tips, you will be well-prepared to showcase your skills and fit for the Data Analyst role at Handshake. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Analyst interview at Handshake. The interview process will likely focus on your analytical skills, experience with SQL, and your ability to derive actionable insights from data. Be prepared to discuss your past experiences, technical skills, and how you can contribute to the company's goals.

SQL and Data Manipulation

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 effectively.

How to Answer

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

Example

“INNER JOIN returns only the rows that have matching values in both tables, while LEFT JOIN returns all rows from the left table and the matched rows from the right table. For instance, 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 optimize a slow-running SQL query?

Performance optimization is key in data analysis, especially when dealing with large datasets.

How to Answer

Discuss techniques such as indexing, query restructuring, and analyzing execution plans to improve performance.

Example

“To optimize a slow-running SQL query, I would first analyze the execution plan to identify bottlenecks. Then, I might add indexes to frequently queried columns or rewrite the query to reduce complexity. For example, using subqueries instead of multiple joins can sometimes enhance performance.”

3. Describe a complex SQL query you wrote and the problem it solved.

This question assesses your practical experience and problem-solving skills.

How to Answer

Provide context about the data, the challenge you faced, and how your query addressed it.

Example

“I once wrote a complex SQL query to analyze customer churn by joining multiple tables, including customer demographics and transaction history. The query calculated churn rates by segmenting customers based on their last purchase date and provided insights that helped the marketing team tailor retention strategies.”

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 give examples of scenarios where they are useful.

Example

“Window functions allow you to perform calculations across a set of rows related to the current row without collapsing the result set. For instance, I used a window function to calculate a running total of sales over time, which helped the sales team track performance trends without losing individual transaction details.”

5. 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 such as imputation, removal, or using algorithms that support missing values.

Example

“When dealing with missing data, I first assess the extent and pattern of the missingness. Depending on the situation, I might use imputation techniques, such as filling in missing values with the mean or median, or I might choose to remove rows or columns if the missing data is significant. For example, in a customer dataset, if a few entries are missing age data, I might impute the average age to maintain the dataset's integrity.”

A/B Testing and Experimentation

1. Explain the concept of A/B testing and its importance in data analysis.

A/B testing is a fundamental method for evaluating the effectiveness of changes in products or marketing strategies.

How to Answer

Define A/B testing and discuss its role in decision-making.

Example

“A/B testing involves comparing two versions of a variable to determine which one performs better. It’s crucial in data analysis because it allows teams to make data-driven decisions. For instance, I conducted an A/B test on two email marketing campaigns to see which subject line generated a higher open rate, leading to more effective marketing strategies.”

2. How do you determine the sample size for an A/B test?

Sample size determination is critical for the validity of A/B test results.

How to Answer

Discuss factors that influence sample size, such as desired statistical power and effect size.

Example

“To determine the sample size for an A/B test, I consider the desired statistical power, typically 80%, and the minimum effect size I want to detect. I also take into account the baseline conversion rate. Using statistical power analysis tools, I can calculate the necessary sample size to ensure the results are statistically significant.”

3. What metrics would you track in an A/B test?

Identifying the right metrics is essential for evaluating the success of an A/B test.

How to Answer

Mention key performance indicators (KPIs) relevant to the test and how they align with business goals.

Example

“In an A/B test for a landing page, I would track metrics such as conversion rate, bounce rate, and average time on page. These metrics help assess user engagement and the effectiveness of the changes made, ultimately aligning with our goal of increasing conversions.”

4. Describe a time when an A/B test did not yield the expected results. What did you learn?

This question evaluates your ability to learn from failures and adapt.

How to Answer

Share a specific example, focusing on the insights gained and how you applied them in future tests.

Example

“I once ran an A/B test on a new feature that I expected would increase user engagement, but the results showed no significant difference. I learned the importance of user feedback and qualitative data. After analyzing user behavior, I realized that the feature was not intuitive, leading me to prioritize user experience in future tests.”

5. How do you ensure the validity of your A/B test results?

Ensuring validity is crucial for making reliable conclusions from A/B tests.

How to Answer

Discuss randomization, control groups, and avoiding biases.

Example

“To ensure the validity of my A/B test results, I randomize the assignment of users to control and treatment groups to eliminate selection bias. I also ensure that the test runs for a sufficient duration to account for variations in user behavior over time, which helps in obtaining reliable results.”

Business Metrics and Analytics

1. How do you define and measure key performance indicators (KPIs) for a marketing campaign?

Understanding KPIs is essential for evaluating the success of marketing efforts.

How to Answer

Discuss the process of selecting relevant KPIs based on campaign goals.

Example

“I define KPIs based on the specific objectives of the marketing campaign. For instance, if the goal is to increase brand awareness, I might track metrics like reach and impressions. For lead generation campaigns, I would focus on conversion rates and cost per lead. This targeted approach ensures that we measure what truly matters for each campaign.”

2. Can you describe a time when your analysis directly influenced a business decision?

This question assesses your impact on the organization through data analysis.

How to Answer

Provide a specific example where your insights led to actionable changes.

Example

“During a quarterly review, I analyzed customer feedback and sales data, revealing that a significant portion of our customers were dropping off during the onboarding process. I presented these findings to the product team, which led to a redesign of the onboarding experience, resulting in a 20% increase in customer retention over the next quarter.”

3. What tools do you use for data visualization, and why?

Data visualization tools are essential for presenting insights effectively.

How to Answer

Mention specific tools you are proficient in and their advantages.

Example

“I primarily use Tableau for data visualization because of its user-friendly interface and powerful capabilities for creating interactive dashboards. I also use Looker for its integration with our data warehouse, allowing for real-time data analysis and reporting, which is crucial for making timely business decisions.”

4. How do you approach data storytelling?

Data storytelling is vital for communicating insights to stakeholders.

How to Answer

Discuss the importance of context and clarity in presenting data.

Example

“When approaching data storytelling, I focus on the narrative behind the data. I start by understanding the audience and their needs, then I present the data in a clear and engaging way, using visuals to highlight key points. For example, when presenting quarterly results, I not only show the numbers but also explain the trends and implications for future strategy.”

5. Describe a situation where you had to work with cross-functional teams. How did you ensure effective collaboration?

Collaboration is key in a data analyst role, especially when working with various departments.

How to Answer

Share your experience in fostering communication and alignment among teams.

Example

“In a previous role, I collaborated with the marketing and sales teams to analyze lead conversion rates. I scheduled regular check-ins to discuss findings and gather input from both teams, ensuring everyone was aligned on goals. This collaborative approach not only improved our analysis but also fostered a sense of ownership across teams, leading to more effective strategies.”