Lime Business Intelligence Interview Questions + Guide in 2025

Overview

Lime is a leading provider of innovative mobility solutions, focused on creating a sustainable future through eco-friendly transportation options.

The Business Intelligence role at Lime involves analyzing complex data sets to drive strategic decision-making and improve business performance. Key responsibilities include developing and maintaining dashboards and reports, conducting in-depth data analysis to identify trends and insights, and collaborating with various departments to support data-driven initiatives. Candidates should possess strong analytical skills, proficiency in data visualization tools, and a deep understanding of business metrics. Exceptional communication skills are essential to effectively present findings to stakeholders and to foster a data-centric culture across the organization. A great fit for this role would be someone who is not only technically proficient but also aligns with Lime's commitment to sustainability and innovation.

This guide will help you prepare for your interview by providing insights into the role’s expectations and necessary skills, allowing you to showcase your strengths and fit for the company’s culture.

Challenge

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Lime Business Intelligence Interview Process

The interview process for a Business Intelligence role at Lime is structured to assess both technical skills and cultural fit within the company. The process typically unfolds as follows:

1. Initial HR Screening

The first step in the interview process is a 30-minute phone call with a recruiter from the HR team. This conversation serves as an introduction to the role and the company, allowing the recruiter to gauge your interest and alignment with Lime's values. During this call, you will discuss your background, relevant experiences, and career aspirations. It’s also an opportunity for you to ask questions about the company culture and the specifics of the Business Intelligence role.

2. Technical Assessment

Following the initial screening, candidates may be invited to participate in a technical assessment. This step can take the form of a video interview where you will be evaluated on your analytical skills, data interpretation, and proficiency with relevant BI tools and technologies. Expect to engage in discussions about your previous projects, methodologies used, and how you approach data-driven decision-making.

3. Onsite Interviews

The final stage of the interview process typically consists of onsite interviews, which may be conducted virtually or in person. This phase usually includes multiple rounds of interviews with various team members, including data analysts, BI managers, and possibly cross-functional stakeholders. Each interview will focus on different aspects of the role, such as data modeling, reporting, and strategic insights, as well as behavioral questions to assess your problem-solving abilities and teamwork skills. Each session generally lasts around 45 minutes, allowing for in-depth discussions.

As you prepare for your interviews, it’s essential to familiarize yourself with the types of questions that may arise during this process.

Lime Business Intelligence Interview Questions

Practice for the Lime Business Intelligence interview with these recently asked interview questions.

QuestionTopicDifficulty
SQL
Hard

Lime’s consumer app team wants to measure the number of users who return to the app after their initial login. Given a user_logins table that records each user’s login by date, write a query to produce a daily summary with retention and engagement metrics.

For each date that appears in the user_logins table, calculate daily retention metrics including cohort size, 7-day retention, retention rate, and rolling 7-day active users. (A cohort for a given date consists of all users who logged in on that specific date (regardless of whether it was their first login or a subsequent login).

Notes:

  • A user is considered retained if they log in again within the next 7 days after their cohort date (days 2-8), excluding the cohort day itself.
  • The rolling 7-day users includes all distinct users who logged in during the 7-day window ending on the current date (from login_date - 6 days to login_date, inclusive).
  • Each (user_id, login_date) pair is unique - users can only log in once per day. The output should be ordered by login_date ascending.
  • Retention rate should be calculated as: (retained_within_7d / cohort_users) * 100, rounded to 1 decimal place.

Schema:

Input:

user_logins table

Column Type
user_id INTEGER
login_date DATE

Output:

Column Type
login_date DATE
cohort_users INTEGER
retained_within_7d INTEGER
retention_rate DECIMAL(3, 2)
rolling_7d_users INTEGER

Example:

Input:

user_id login_date
1 2024-06-01
2 2024-06-01
1 2024-06-02
2 2024-06-08
3 2024-06-08
1 2024-06-09
3 2024-06-10

Output:

login_date cohort_users retained_within_7d retention_rate rolling_7d_users
2024-06-01 2 1 50.0 2
2024-06-02 1 1 100.0 2
2024-06-08 2 1 50.0 2
2024-06-09 1 0 0.0 2
2024-06-10 1 0 0.0 2

Explanation:

Retention is calculated for users who return within 7 days after their first login on a given day. Rolling 7-day user counts unique users over the current and previous 6 days.*

Suppose the current login_date is October 10, 2027.

  • The cohort for this date includes all users who logged in on 2027-10-10.
  • A user is considered retained if they log in again between 2027-10-11 and 2027-10-17 (inclusive).
  • The cohort day itself (2027-10-10) is excluded from this retention window.
Statistics
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Statistics
Easy
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