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REQUIREMENT_ID_96 β€’ 3-DAY_ACTIVE_POLICY

Data Analyst – Business Finance

Company
Company Jobera
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary Not disclosed
Location
Location Bangalore
Posted Date
Posted Date Today
SQL Python AI tools Claude PowerBI Tableau data visualization data cleaning analytical thinking communication problem solving
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Aptitude and Logical Reasoning Practice Open Resource β†—
Helps sharpen problem‑solving skills essential for data analysis interviews.
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Interview Preparation Guide Open Resource β†—
Provides insights into common interview questions and best practices for technical interviews.
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Interview Preparation Resources Open Resource β†—
Offers mock interview scenarios and study plans for data analyst roles.
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Coding Practice Platform Open Resource β†—
Enhances coding proficiency in Python and SQL for technical assessments.

Bachelor's degree in Computer Science, Information Technology, Engineering, Finance, or related fields. Minimum 60% aggregate in the final year or equivalent. No backlogs. Freshers with 0–2 years of experience in reporting, analytics, or AI are eligible.

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Round 1: Technical – SQL, Python, AI tools
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Round 2: Technical – case study, data modeling, business acumen
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Round 3: HR
Razorpay, a leading fintech company headquartered in Bangalore, has revolutionized online payments in India by providing a seamless, developer‑friendly payment gateway that powers millions of merchants worldwide. With a mission to simplify digital commerce, Razorpay has grown from a startup to a unicorn, earning recognition for its innovative product suite, customer‑centric approach, and a culture that encourages experimentation and rapid learning. The Data Analyst – Business Finance role is a pivotal position that bridges finance and technology. As a key member of the finance team, you will be responsible for designing, automating, and maintaining business reports that drive strategic decision‑making. Your work will involve building AI‑powered workflows to streamline data collection, cleaning, and analysis, ensuring that every financial metric is accurate and actionable. Key responsibilities include: 1. Developing automated dashboards and reports for finance stakeholders using SQL and Python. 2. Collaborating with cross‑functional teams to define data requirements and business KPIs. 3. Implementing AI tools such as Claude to enhance data processing and predictive analytics. 4. Validating data integrity and performing root‑cause analysis for discrepancies. 5. Creating data visualizations in PowerBI or Tableau to communicate insights. 6. Documenting data pipelines, processes, and best practices. 7. Identifying opportunities for process automation and efficiency gains. 8. Supporting ad‑hoc financial analysis and forecasting. 9. Ensuring compliance with data governance and security standards. 10. Mentoring junior analysts and sharing knowledge across the team. Tech stack: SQL, Python, Claude (AI), PowerBI, Tableau, data cleaning libraries (pandas, numpy), version control (Git). Growth path: Starting as a junior analyst, you can progress to senior analyst, lead analyst, or data science roles, eventually moving into product or finance leadership positions. Why join Razorpay? The company offers a collaborative environment where ideas are valued, a strong emphasis on continuous learning, and exposure to cutting‑edge fintech solutions. Employees enjoy flexible work hours, regular hackathons, and a culture that balances ambition with well‑being. If you thrive on solving complex problems and want to impact millions of users, Razorpay is the place to build a rewarding career.

Jobera β€” Data Analytics & SQL Interview Guide

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

APTITUDE & LOGIC πŸ“–
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 β†—
COMPANY GUIDE 🎯
Jobera Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Jobera and off-campus tech roles.

Open Resource β†—
MOCK TESTS & PAPERS πŸ“
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource β†—
CODING PRACTICE πŸ’»
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource β†—
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, AI tools, Claude, PowerBI, Tableau, data visualization, data cleaning, analytical thinking, communication, problem solving) & 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
Jobera 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 β–Ό
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 β–Ό
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 β–Ό
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 β–Ό
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 β–Ό
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 Jobera as a Data Analyst – Business Finance?
Preparation Tip: Highlight Jobera'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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