Here I would like to share a newly released Ramp interview experience, the position is Data Analyst.
I got it in November 2025. Ramp Candidates for the Data Analyst offer have previously worked as data analysts in a Fintech company for two years, mainly responsible for expenditure optimization and user behavior analysis.
The entire Ramp interview process took about three weeks from submitting your resume to signing the written offer. The pace was very fast, but the overall pressure was quite high. In line with feedback from many people, Ramp's Data Analyst interview is not simply a test of SQL proficiency, but a strong emphasis on business understanding, how data analysis supports decision-making, and whether you can truly think about issues from a product and business perspective.
Timeline record (real schedule)
- 2025-11-05: I submitted my resume on LinkedIn and received an email from Recruiter that evening, asking for HR screening.
- 2025-11-07: HR Phone Screening (15 minutes)
- 2025-11-12: Hiring Manager Interview (50 minutes)
- 2025-11-18: Technical Screen (60 minutes, SQL + product analysis)
- 2025-11-25 & 11-26：Virtual Onsite
- Data Challenge (90 minutes)
- Two rounds of team meetings
- A round of Culture Fit
- 2025-12-02: Received verbal offer
- 2025-12-05: Signed written offer
HR/Recruiter Phone Screening (15 minutes)
This round was very quick, the recruiter was friendly and basically confirmed the background and motivation.
Major issues include:
- 30 seconds to introduce yourself
I focused on my Fintech background, spend optimization projects, and why I was interested in Ramp, a product with cost savings as its core value. - Why Ramp
My answer mainly focused on Ramp’s core selling points, such as helping customers reduce their expenditure ratio, automating the monthly settlement process, etc., and combined it with the reimbursement process optimization project I had done at work before, emphasizing that I wanted to put similar experience on a larger platform. - Briefly chatted about salary expectations and entry time
At the end, I asked about the biggest challenge of the data team at present, and she mentioned a keyword: how to scale data insights so that more students with non-data background can directly use it.
Hiring Manager Interview (50 minutes)
1. Project Digging: From Data to Business Results
The interviewer in this round was the head of the data team. The questions almost all revolved around the projects on my resume, and the questions were very detailed.
Main concerns include:
- How to define core indicators
In the reimbursement process optimization project I mentioned, I divided the indicators into primary and secondary. The primary is whether the reimbursement cycle is shortened, the secondary is whether the compliance rate is improved, and whether the manual review burden is reduced. - How to handle abnormal data
The interviewer is more concerned about whether I understand the business behind the data. I will give an example. We usually use quantiles or IQR to find outliers, and then perform manual verification based on specific vendors or departments, rather than directly eliminating them mechanically. - Ultimate business impact
He repeatedly asked whether the analysis results really changed the decision-making, such as whether it saved how much manpower time, or whether it promoted changes in products or processes.
2. Behavioral question: Experience in using data to influence decision-making
The question is to describe a time when you used data to influence product decisions.
I answered with a relatively complete STAR structure. Starting from the problem of declining user retention, through cohort analysis, I found that users lacked reminders at a certain key node, and finally pushed the product to launch corresponding functions. The result is a significant improvement in retention rates.
3. Simple business case
There is also a partial case question:
If Ramp launched a new automatic expense classification feature, how would you measure its impact on user retention.
My answer is:
- Clearly define core metrics, such as 30-day retention
- Set auxiliary indicators, such as reimbursement submission rate and classification error rate
- Design A/B tests to control confounding factors such as company size and industry
- Set guardrail metrics to ensure user experience is not broken
Technical Screen (60 minutes)
1. SQL interview questions (business scenario)
This round is a typical Ramp style SQL interview. The interviewer gives business background directly on CoderPad instead of algorithm questions.
The topic is roughly:
Given a transaction table and a department table, calculate the monthly spending growth rate for each department and find the top three departments with the largest decrease in growth rate.
My idea of solving the problem at the time was:
- Start by aggregating spending by department and month
- Use window function to calculate expenses for the previous month
- Calculate the month-on-month change rate and sort
The SQL code for subsequent review is as follows:
WITH monthly_spend AS (
SELECT
d.department_name,
DATE_TRUNC('month', t.transaction_date) AS month,
SUM(t.amount) AS total_spend
FROM transactions t
JOIN departments d
ON t.department_id = d.department_id
GROUP BY d.department_name, month
),
labeled AS (
SELECT
department_name,
month,
total_spend,
LAG(total_spend) OVER (
PARTITION BY department_name
ORDER BY month
) AS prev_spend
FROM monthly_spend
)
SELECT
department_name,
month,
ROUND(
(total_spend - prev_spend) / prev_spend * 100,
2
) AS mom_growth_pct
FROM labeled
WHERE prev_spend IS NOT NULL
ORDER BY mom_growth_pct ASC
LIMIT 3;
2. Product/experimental analysis issues
The SQL is followed by product analysis questions such as:
How to design an A/B test to evaluate the impact of a new reimbursement reminder feature on compliance rates.
What the interviewer focuses on is not statistical formulas, but:
- Are the assumptions clear?
- Are metrics aligned with business goals?
- Have sample size, bias, and potential side effects been considered?
Virtual Onsite (core link)
1. Data Challenge (90 minutes)
This is the most challenging round of the entire process.
A data set (CSV, about 100,000 rows of transaction records) close to real business was given, and the analysis was required to be completed within a limited time, and a short presentation was given at the end.
My overall steps are:
- Quick EDA to understand the distribution of categories, vendors, and departments
- Anomaly detection by department and vendor
- Combined with the time dimension, observe changes in expenditure trends
2. Key insights and business recommendations
In the end, my core findings were:
A department's spending on a specific vendor has increased significantly year-on-year, possibly related to contract price adjustments.
Based on this finding, I made recommendations to trigger renegotiation and estimated potential savings.
During the presentation stage, the interviewer repeatedly stressed that:
Don’t stop at the analysis itself, but identify what business actions can be taken next.
3. Other rounds: Team and culture
The remaining rounds focused on cross-team collaboration and cultural fit.
Discussions include:
- How to cooperate with product and engineering classmates
- How to prioritize when resources are limited
- Whether you can adapt to Ramp’s relatively flat team culture that emphasizes ownership
Ramp DA Offer I really recommend it after winning it.
After I got the Ramp DA offer, many friends sent me private messages asking me about preparation details. In addition to the interview experience I shared earlier, I had to take advantage of the biggest "plug-in" for this interview preparation - the real-time interview service of interview assistance/VO assistance/interview assist.
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