Required Skills & Tech Stack
SQL
Python
Tableau
Power BI
Pandas
Data Warehousing
📚 Free Study Materials (4)
📖
Aptitude Practice Questions & Mock Tests
Open Resource ↗
Curated logical, quantitative, and verbal reasoning problems for the initial online screening round.
🎯
Company-Specific Interview Preparation Corner
Open Resource ↗
Detailed interview experiences, exam formats, and previous test questions for Razorpay and tech roles.
📝
Technical Placement Cheat Sheets & Question Bank
Open Resource ↗
High-yield coding cheat sheets, core CS fundamentals (OOP, DBMS, OS, Networks), and rapid revision guides.
💻
Algorithm, DSA & Live Code Debugger Practice
Open Resource ↗
Hands-on problem sets to improve coding speed and step-by-step memory debugging.
Eligibility Criteria
Experience: 0-2 Years. Open to matching qualifications.
Selection & Interview Process
1
Online Coding Assessment / Technical Interview Rounds / HR Review
Job Description & Key Responsibilities
Razorpay Payments Analytics group is hiring Data Analysts. You will build merchant transaction dashboards, write advanced SQL queries, analyze checkout conversion funnels, and generate actionable business intelligence for fintech products.
Preparation Hub
Razorpay — Data Analytics & SQL Interview Guide
Previously asked questions, exam syllabus, coding benchmarks & round strategy.
📖 Previously Asked Questions & Preparation Links
APTITUDE & LOGIC
📖
Aptitude Practice Questions & Online Mock Tests
Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.
Open Resource ↗
COMPANY GUIDE
🎯
Razorpay Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Razorpay and off-campus tech roles.
Open Resource ↗
MOCK TESTS & PAPERS
📝
Comprehensive Interview Prep Resources & Syllabus
Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.
Open Resource ↗
CODING PRACTICE
💻
Algorithm and Data Structure Problem Set
Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.
Open Resource ↗
💻 Previously Asked Coding Challenges
Easy / Medium
⚡ Live Debugger
Two Sum & Two Pointers Array Optimization
Asked in 85% tech rounds
Medium
⚡ Live Debugger
Reverse Linked List & Detect Cycle (Floyd's Algorithm)
Frequently asked in core rounds
Medium
⚡ Live Debugger
SQL Nth Highest Salary with DENSE_RANK() & Subqueries
Top standard database question
Easy / Medium
⚡ Live Debugger
Valid Parentheses & Stack Implementation
Standard online assessment problem
🗺️ Round-Wise Strategy
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (SQL, Python, Tableau, Power BI, Pandas, Data Warehousing) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Razorpay Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
❓ Core Technical Questions & Answers
What is the difference between WHERE and HAVING clauses in SQL? Answer ▼
Model Answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer ▼
Model Answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer ▼
Model Answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer ▼
Model Answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer ▼
Model Answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
🤝 Behavioral & HR Questions (STAR Method)
Why do you want to join Razorpay as a Data Analyst / Associate Analytics Engineer?
Preparation Tip: Highlight Razorpay's market reputation, recent tech innovations, and how your skills in SQL directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
