Epigamia Data Analyst Interview: Rounds, Questions and Preparation

What the job involves

As a Data Analyst at an Indian unicorn or high-growth startup like Epigamia, you will work with large datasets to extract insights that inform product, marketing, and supply chain decisions. Day to day, you might build dashboards, run SQL queries, analyse customer behaviour, and present findings to cross-functional teams. The role demands a mix of technical skills and business acumen to support fast-paced, data-driven decisions.

Role and Technical Knowledge

1. 1

   How would you write a SQL query to find the top 5 selling products by revenue in the last quarter?

   Strong answer uses GROUP BY, SUM, ORDER BY, and LIMIT, and mentions filtering by date. Also explains assumptions about revenue calculation.

2. 2

   Explain the difference between INNER JOIN and LEFT JOIN with an example relevant to customer orders.

   Clarify that INNER JOIN returns only matching rows, while LEFT JOIN returns all rows from the left table. Use an example like customers and orders to show when to use each.

3. 3

   What steps would you take to clean a messy dataset with missing values and outliers?

   Cover identifying missing values, deciding to impute or drop, handling outliers via capping or transformation, and documenting decisions. Mention tools like Pandas or Excel.

4. 4

   How do you measure the success of a new product launch using data?

   Discuss defining KPIs like adoption rate, repeat purchase, and revenue. Suggest tracking cohorts and comparing against benchmarks or control groups.

Practical Scenarios

1. 5

   You are asked to analyse why sales dropped in a particular region last month. How would you approach this?

   Outline a structured approach: check data quality, segment by time, product, and channel, compare with other regions, and look for external factors. Suggest hypotheses and validation.

2. 6

   If you had to build a dashboard for the marketing team to track campaign performance, what metrics would you include and why?

   Mention metrics like CTR, conversion rate, CAC, ROAS, and engagement. Explain how each helps marketing optimise spend and targeting.

3. 7

   How would you design an A/B test to check if a new checkout flow increases conversion?

   Cover random assignment, control and test groups, sample size calculation, duration, and success metrics. Mention avoiding common pitfalls like peeking.

4. 8

   You notice a sudden spike in customer complaints about a product. How would you use data to investigate?

   Suggest analysing complaint text, correlating with product batches, time, and regions. Use SQL to filter and visualise trends, and recommend next steps.

Behavioural and HR

1. 9

   Tell me about a time you used data to solve a problem. What was the outcome?

   Use STAR method: describe situation, task, action, and result. Highlight data tools used and impact on a project or team.

2. 10

   Why do you want to work as a data analyst at a fast-growing startup like Epigamia?

   Show passion for data-driven decision-making in a dynamic environment. Mention specific aspects of startups like ownership, speed, and learning opportunities.

3. 11

   Describe a situation where you had to explain a technical concept to a non-technical person.

   Emphasise clarity, avoiding jargon, using visuals, and checking understanding. Give an example from a project or coursework.

4. 12

   How do you handle tight deadlines or multiple priorities?

   Talk about prioritisation, time management, and communication. Give an example of a time you managed multiple tasks effectively.

Company and Sector Awareness

1. 13

   What metrics would you track to measure the health of a D2C food brand like Epigamia?

   Suggest metrics like customer acquisition cost, repeat purchase rate, average order value, and churn. Relate to growth and profitability.

2. 14

   How can data analytics help a startup optimise its supply chain?

   Discuss demand forecasting, inventory management, and logistics optimisation. Use examples like reducing wastage or improving delivery times.

3. 15

   In your opinion, what are the key differences between working as a data analyst at a startup versus a large corporation?

   Highlight broader responsibilities, faster pace, direct impact, and resource constraints. Show awareness of startup culture.

4. 16

   If you were to improve customer retention for a subscription-based product, what data would you look at?

   Mention cohort analysis, churn prediction, engagement metrics, and feedback. Suggest actionable insights like targeted offers.