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REQUIREMENT_ID_1357 • 3-DAY_ACTIVE_POLICY JD

Data Analyst, Business Finance

Company
Company Razorpay
Type
Opportunity Type Internship
Salary
Stipend / Salary 6 LPA – 12 LPA (depending on experience and skill set)
Location
Location Bengaluru, India
Posted Date
Posted Date Today
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SQL Python Claude PowerBI Tableau Data Visualization Data Cleaning Financial Analysis AI Prompt Engineering Excel
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Aptitude Practice Questions Open Resource ↗
Comprehensive set of quantitative and logical reasoning problems to sharpen the analytical skills needed for the online assessment.
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Razorpay Recruitment Process Insights Open Resource ↗
First‑hand experiences and tips from candidates who have cleared Razorpay’s interview stages, useful for preparation strategy.
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Data Analyst Interview Preparation Guide Open Resource ↗
Curated notes on typical data‑analyst interview questions, SQL queries, Python exercises and BI tool concepts relevant to Razorpay.
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Algorithm & Data Structure Problem Set Open Resource ↗
A collection of coding problems to practice Python implementation and problem‑solving speed for the technical round.

• Minimum 0‑2 years of experience in reporting, analytics or AI. • Bachelor’s degree in Engineering, Computer Science, Statistics, Economics, Mathematics, Commerce or an MBA. • CGPA/percentage ≥ 6.5/70% (or equivalent). • No active backlogs at the time of joining. • Strong analytical mindset, attention to detail and ability to work independently. • Willingness to work from the Bengaluru office.

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Round 1: Online assessment (SQL & Python coding + AI tool basics)
2
Round 2: Technical interview (case studies, data modeling, BI tool usage)
3
Round 3: HR interview (cultural fit, career aspirations).
Razorpay is one of India’s premier full‑stack fintech companies, founded in 2014 with the vision of simplifying payments for Indian businesses. Over the past decade the company has grown from a startup to a $180+ billion annualized transaction processor, serving more than 100 unicorns and a host of global brands such as Airbnb, Facebook, Zomato and Swiggy. With a presence across India, Singapore and Malaysia, Razorpay’s product suite spans checkout, payroll automation, invoicing, smart payouts, AI‑driven fraud detection and real‑time risk intelligence. The firm is backed by marquee investors like GIC, Tiger Global and Mastercard, and continues to expand through strategic acquisitions such as Ezetap and BillMe. The Business Finance team sits at the intersection of finance, product and data, enabling the company to make data‑driven decisions that fuel growth. As a Data Analyst – Business Finance you will partner with finance leaders, product managers and engineering to translate raw data into actionable insights, build automated reporting pipelines and embed AI‑powered workflows that reduce manual effort. This role is ideal for recent graduates or early‑career professionals who love numbers, are comfortable with SQL and Python, and are eager to experiment with generative AI tools like Claude to accelerate analysis. **Key Responsibilities** 1. Design, develop and maintain automated business dashboards and financial reports using PowerBI/Tableau. 2. Extract, transform and load (ETL) data from multiple sources (transactional DBs, data lakes, third‑party APIs) using SQL and Python scripts. 3. Validate data quality and ensure accuracy of financial metrics such as revenue, churn, cash‑flow and cost‑to‑serve. 4. Build AI‑enabled workflows (e.g., Claude prompts) to automate repetitive analysis tasks and generate insights on demand. 5. Conduct ad‑hoc deep‑dive analyses to support budgeting, forecasting and strategic initiatives. 6. Collaborate with product and engineering teams to define data requirements and improve data pipelines. 7. Identify process inefficiencies and propose automation or optimization solutions. 8. Prepare concise executive summaries and visual storytelling for senior leadership. 9. Stay updated on emerging analytics tools, AI capabilities and fintech trends to continuously enhance the analytics stack. 10. Mentor junior analysts or interns on best practices in data handling and reporting. **Tech Stack**: SQL, Python (pandas, numpy), Claude (or other LLMs), PowerBI, Tableau, Git, JIRA, Excel, basic knowledge of cloud data warehouses (Snowflake/BigQuery). **Growth Path**: Starting as a Data Analyst, you can progress to Senior Analyst, Finance Business Partner, and eventually Finance Manager or Head of Business Analytics, with ample opportunities to lead cross‑functional projects and influence product strategy. **Why Join Razorpay?** Razorpay’s culture prizes ownership, autonomy and relentless learning. You will work alongside industry veterans, get direct exposure to high‑impact financial products, and have the freedom to experiment with cutting‑edge AI tools. The company’s commitment to diversity, transparent communication and rapid career progression makes it an ideal launchpad for ambitious analysts.

Razorpay — Data Analytics & SQL Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

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 ↗
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, Claude, PowerBI, Tableau, Data Visualization, Data Cleaning, Financial Analysis, AI Prompt Engineering, Excel) & 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.
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.
Why do you want to join Razorpay as a Data Analyst, Business Finance?
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.

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