Zeptonow Logistics Data Analyst Interview: Rounds, Questions and Preparation

What the job involves

A data analyst at an Indian unicorn or high-growth startup like Zeptonow Logistics turns raw operational, product, and customer data into dashboards, reports, and recommendations that drive quick decisions. Day to day you write SQL, build Python or spreadsheet analyses, create dashboards in tools like Power BI or Tableau, and present findings to category, supply chain, or growth teams. The pace is fast, data is messy, and you are expected to move from question to insight in hours or days, not weeks.

Role and technical knowledge

1. 1

   Write a SQL query to find the top 5 cities by number of orders delivered late in the last 30 days.

   A strong answer uses a WHERE clause on delivery date and late flag, GROUP BY city, ORDER BY count DESC, and LIMIT 5. Mention handling null delivery dates or cancelled orders.

2. 2

   How would you calculate the average delivery time per order for a quick commerce company, and what data fields would you need?

   Cover order placed timestamp, delivery completed timestamp, and order status. Explain filtering to only delivered orders and using TIMESTAMPDIFF or equivalent in SQL.

3. 3

   What is the difference between a left join and an inner join, and when would you use each in an orders and customers dataset?

   Explain that inner join keeps only matching rows while left join keeps all left table rows. Give an example like finding customers with no orders using a left join and null check.

4. 4

   Explain a window function like ROW_NUMBER or RANK and give a use case for ranking delivery partners by on-time rate.

   Define that window functions compute across a set of rows without collapsing them. Show PARTITION BY city ORDER BY on_time_rate DESC and ROW_NUMBER to pick the top partner per city.

Practical scenarios and case questions

1. 5

   Daily active users on a grocery app dropped 15 percent week over week. How would you investigate this as a data analyst?

   Strong answers segment by city, platform, new vs existing users, and funnel step. Mention checking data pipeline issues first, then external factors like holidays or competitor launches.

2. 6

   You are asked to measure the impact of a new 10 minute delivery promise on customer retention. What analysis would you do?

   Propose a before and after comparison or an A/B test, define retention clearly, and control for city, user tenure, and order frequency. Mention statistical significance and avoiding seasonality bias.

3. 7

   The operations team says late deliveries increased but your dashboard shows no change. How do you resolve this conflict?

   Suggest checking definitions of late, date ranges, time zones, and data freshness. Propose a shared metric definition and a quick manual validation on a sample of orders.

4. 8

   How would you build a simple dashboard for category managers to track top selling products and stockouts?

   Cover key metrics like units sold, revenue, stockout rate, and days of inventory. Explain filters by category and city, and a refresh schedule that matches business needs.

Behavioural and situational questions

1. 9

   Tell me about a time you found an error in your own data analysis and how you fixed it.

   Use a clear situation, action, and result. Show ownership, how you validated the fix, and what process you changed to prevent repeats.

2. 10

   Describe a project where you had to explain a technical finding to a non technical person.

   Focus on simplifying without losing accuracy, using a chart or one line summary, and checking understanding. Mention the outcome like a decision that changed.

3. 11

   You have three urgent requests from different teams and limited time. How do you prioritise?

   Explain asking about business impact, deadlines, and effort. Suggest a quick scoring method and communicating trade offs transparently to stakeholders.

4. 12

   Why do you want to work as a data analyst at a fast growing startup rather than a large established company?

   Show comfort with ambiguity, speed, and broad ownership. Link to a real example of learning quickly or building something from scratch.

HR and company fit questions

1. 13

   What do you know about the quick commerce or on demand delivery sector in India?

   Mention dark stores, 10 to 30 minute delivery, unit economics, and competition. Show you understand that data drives slotting, inventory, and rider allocation.

2. 14

   How do you keep your SQL and analytics skills current outside of college coursework?

   Name specific resources like practice platforms, Kaggle datasets, or a personal dashboard project. Show consistent practice, not just a one time course.

3. 15

   Are you comfortable working with messy real world data and tight deadlines? Give an example.

   Share a college project, internship, or competition where data was incomplete or deadlines were short. Highlight what you did to deliver a usable result anyway.

4. 16

   Where do you see yourself in two years as a data analyst?

   Show realistic growth like owning a business metric, mentoring interns, or moving into a specialised analytics area. Tie it to learning fast in a startup environment.