GitHub Data Analyst Interview Questions + Guide in 2024

GitHub stands as one of the leading platforms in software development, fostering collaboration and innovation worldwide. Renowned for its robust version control and code repository services, GitHub has been a game-changer in how developers and teams code together.

As a data analyst at GitHub, you will dive into data-driven insights to support strategic business decisions. Your role will encompass technical skills like SQL and data architecture, and a keen ability to collaborate with cross-functional teams.

In this guide, we will walk you through GitHub’s interview process, including common GitHub data analyst interview questions and valuable tips.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the GitHub Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for GitHub data analyst role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage often focus on GitHub’s data systems, SQL queries, and coding challenges.

In the case of data analyst roles, live coding assessments involving SQL queries are administered. You may be given data tables to query and asked to solve problems in a limited time while explaining your approach. This test often assesses your proficiency in writing efficient queries and understanding data architecture.

Additionally, questions regarding data visualization, data analysis strategies, and scenario-based questions like “How would you handle a situation where you disagree with a colleague on the implementation of a feature?” may also be present. Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the GitHub office. Your technical prowess, including programming and data analysis capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the data analyst role at GitHub.

Typically, interviews at Github vary by role and team, but commonly Data Analyst interviews follow a fairly standardized process across these question topics.

1. Would you suspect anything unusual about the A/B test results with 20 variants?

Your manager ran an A/B test with 20 different variants and found one significant result. Would you consider this result suspicious?

2. How would you set up an A/B test to optimize button color and position for higher click-through rates?

A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

3. What steps would you take if friend requests on Facebook dropped by 10%?

A product manager at Facebook reports a 10% decrease in friend requests. What actions would you take to investigate and address this issue?

4. Why might job applications be decreasing despite stable job postings?

You observe that the number of job postings per day has remained constant, but the number of applicants has been steadily decreasing. What could be causing this trend?

5. What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis?

You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common issues in “messy” datasets.

6. How would you design a machine learning model to classify major health issues based on health features?

You work as a machine learning engineer for a health insurance company. Design a machine learning model that, given a set of health features, classifies whether an individual will undergo major health issues or not.

7. Write a SQL query to select the 2nd highest salary in the engineering department.

Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

8. Write a function to merge two sorted lists into one sorted list.

Given two sorted lists, write a function to merge them into one sorted list. Bonus: Determine the time complexity.

9. Create a function missing_number to find the missing number in an array.

You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

10. Develop a function precision_recall to calculate precision and recall metrics from a 2-D matrix.

Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

11. Write a function to search for a target value in a rotated sorted array.

Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. Write a function to search for a target value in the array and return its index, or -1 if not found. Bonus: Achieve (O(\log n)) runtime complexity.

Here are some quick tips to prepare for your upcoming GitHub’s data analyst interview:

• Be Prepared for Technical Assessments: GitHub’s technical interviews often include SQL coding tests conducted live. Practice SQL interview questions and familiarize yourself with common data structures to improve your speed and accuracy.
• Know GitHub’s Ecosystem: Have a thorough understanding of GitHub’s tools and how data can be utilized to improve business decisions. Be prepared to discuss analytics and data visualization methods relevant to GitHub’s data.
• Communicate Clearly: During behavioral interviews and team discussions, emphasize your collaboration and problem-solving skills. Be prepared to discuss how you handle disagreements or propose improvements within a team setting.

What is the average salary for a Data Analyst at Github?

What sets GitHub’s company culture apart?

GitHub values a collaborative and innovative work environment. The culture fit interviews aim to ensure that new hires can work well in a team setting and align with the company’s values. GitHub also places a high emphasis on transparency and trust among team members.

How did candidates feel about their interview experiences at GitHub?

Candidates’ experiences varied, but many appreciated the professionalism and quick feedback from recruiters. However, some found the technical assessments challenging due to time constraints and the pressure of live coding. Overall, the role and the team left positive impressions on most candidates.

If you’re preparing for a Data Analyst position at GitHub, knowing what to expect can significantly enhance your chances. Our GitHub Interview Guide offers deep insights into the interview process, covering initial HR screenings, technical challenges, and final rounds emphasizing culture fit and collaboration.

Good luck with your interview!