Niantic, Inc. Data Analyst Interview Questions + Guide in 2025

Niantic, Inc. is a pioneer in augmented reality (AR) technology, creating engaging experiences that blend the digital world with the physical environment, most famously known for their hit game Pokémon GO.

The Data Analyst role at Niantic involves analyzing large sets of data to provide actionable insights that inform business strategy and enhance user experiences. Key responsibilities include conducting data wrangling and modeling to extract meaningful patterns, performing A/B testing to evaluate the effectiveness of various game features, and developing SQL queries to analyze gameplay data. A successful candidate will possess strong analytical skills, proficiency in SQL and Python, and an understanding of statistical methods to interpret data effectively. Additionally, a collaborative spirit and a passion for gaming and AR technology will align with Niantic's innovative and community-focused culture.

This guide will help you prepare for your interview by highlighting the essential skills and competencies required for the Data Analyst role, ensuring you are ready to demonstrate your capabilities effectively.

The interview process for a Data Analyst position at Niantic, Inc. is structured to assess both technical skills and cultural fit within the company. The process typically unfolds in several key stages:

1. Initial HR Call

The first step is an initial phone call with a recruiter or HR representative. This conversation usually lasts about 30 minutes and serves to introduce you to the company and the role. The recruiter will inquire about your background, experiences, and motivations for applying, while also providing insights into Niantic's culture and values.

2. Manager Interview

Following the HR call, candidates will have a conversation with the hiring manager. This interview focuses on your relevant experience and how it aligns with the team's goals. Expect to discuss your previous projects, your approach to data analysis, and how you can contribute to Niantic's mission. This is also an opportunity for you to ask questions about the team dynamics and expectations.

3. Onsite Interview

The onsite interview is a comprehensive assessment that includes multiple components. Candidates can expect to face case studies and scenario-based questions that evaluate their analytical thinking and problem-solving abilities. Additionally, there will be a focus on SQL proficiency, where you will be asked to solve data-related problems using SQL queries. This part of the interview is designed to gauge your technical skills in a practical context.

4. Take-Home Assignment

A unique aspect of the interview process is the take-home assignment, which is often considered the most challenging part. This assignment typically involves data wrangling and modeling tasks, requiring candidates to utilize both SQL and Python. The goal is to assess your ability to work independently and apply your analytical skills to real-world data scenarios.

5. Final Interview with Executive

The final step in the interview process is a conversation with an executive or senior leader within the company. This interview is less technical and more focused on your fit within the company culture and your long-term career aspirations. Expect to discuss your vision for the role and how you can contribute to Niantic's strategic objectives.

As you prepare for these stages, it's essential to be ready for the specific types of questions that may arise during the interviews.

In this section, we’ll review the various interview questions that might be asked during a Data Analyst interview at Niantic, Inc. The interview process will assess your technical skills in SQL and Python, as well as your ability to analyze data and derive insights. Be prepared to discuss your experience with A/B testing, data wrangling, and modeling, as well as your approach to solving case-based scenarios.

Technical Skills

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

Clearly define both types of joins and provide a brief example of when you would use each.

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 instance, if I want to list all customers and their orders, I would use a LEFT JOIN to ensure I include customers who haven’t placed any orders.”

2. Describe a challenging SQL query you wrote and how you approached it.

This question assesses your problem-solving skills and your ability to write complex SQL queries.

How to Answer

Discuss the context of the query, the challenge you faced, and the steps you took to resolve it.

Example

“I was tasked with generating a report on customer engagement metrics. The challenge was to aggregate data from multiple sources with different structures. I first mapped out the relationships between the tables, then wrote a series of nested queries to extract and join the necessary data, ensuring I accounted for any discrepancies in the data types.”

3. How do you approach A/B testing, and what metrics do you consider?

A/B testing is a key component of data analysis, especially in product development.

How to Answer

Explain your methodology for designing A/B tests and the importance of selecting the right metrics.

Example

“I start by defining a clear hypothesis and identifying the key performance indicators (KPIs) that will measure success. I ensure that the sample size is statistically significant and that the test runs for an adequate duration to account for variability. Metrics like conversion rate and user engagement are critical to evaluate the impact of changes.”

4. Can you walk us through a data wrangling project you completed?

This question evaluates your experience with data cleaning and preparation, which is essential for analysis.

How to Answer

Describe the project, the data sources involved, and the techniques you used to clean and prepare the data.

Example

“In a recent project, I worked with a dataset containing user activity logs. The data was messy, with missing values and inconsistent formats. I used Python’s Pandas library to clean the data, filling in missing values with appropriate methods and standardizing date formats. This preparation allowed for accurate analysis of user behavior trends.”

Case Studies and Scenarios

5. How would you analyze user engagement for a new feature in a mobile game?

This question tests your analytical thinking and ability to apply data analysis to real-world scenarios.

How to Answer

Outline your approach to defining metrics, collecting data, and analyzing results.

Example

“I would start by defining key metrics such as daily active users, session length, and retention rates. I would then set up tracking to collect data on user interactions with the new feature. After gathering sufficient data, I would perform a comparative analysis against a control group to assess the feature's impact on overall engagement.”

6. Describe a time when you had to present data findings to a non-technical audience.

This question assesses your communication skills and ability to convey complex information simply.

How to Answer

Discuss the context of the presentation, the audience, and how you tailored your message.

Example

“I presented user engagement metrics to the marketing team, who were not familiar with technical jargon. I focused on visualizations to illustrate trends and used straightforward language to explain the implications of the data. This approach helped them understand the insights and make informed decisions about their campaigns.”

7. What steps would you take if you found discrepancies in the data you were analyzing?

This question evaluates your attention to detail and problem-solving skills.

How to Answer

Explain your process for identifying, investigating, and resolving data discrepancies.

Example

“If I found discrepancies, I would first verify the data sources to ensure accuracy. I would then trace back through the data processing steps to identify where the issue occurred. Once located, I would correct the data and document the process to prevent similar issues in the future.”

8. How do you prioritize tasks when working on multiple data projects?

This question assesses your time management and organizational skills.

How to Answer

Discuss your approach to prioritization and how you manage deadlines.

Example

“I prioritize tasks based on their impact on business goals and deadlines. I use project management tools to track progress and communicate with stakeholders to ensure alignment. If necessary, I adjust priorities based on feedback or changes in project scope.”