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REQUIREMENT_ID_1552 β€’ 3-DAY_ACTIVE_POLICY

Analyst, Big Data Analytics & Engineering

Company
Company Mastercard
Type
Opportunity Type Internship
Salary
Stipend / Salary 10 LPA
Location
Location Pune, India
Posted Date
Posted Date Yesterday
SQL Python R Tableau Power BI Excel Data Analysis AI/ML Statistical Analysis Database Management Business Intelligence Communication Presentation Cross-functional Collaboration
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Aptitude Practice for Data Analytics Open Resource β†—
Provides a wide range of aptitude questions that help sharpen analytical thinking, useful for preparing for data‑centric roles at Mastercard.
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Interview Preparation Guide Open Resource β†—
Offers insights into common interview patterns and technical questions that are relevant for roles involving SQL, Python, and analytics.
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Data Analytics Study Resources Open Resource β†—
Contains tutorials, projects, and case studies that help build practical skills in data analysis and business intelligence.
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Coding Practice for Technical Interviews Open Resource β†—
A repository of coding problems that strengthen algorithmic thinking, essential for technical rounds in data analytics positions.

Bachelor’s degree in Computer Science, Data Science, Business Analytics, Economics, Finance, or related field. Minimum 60% marks (or 6.5 CGPA) in the qualifying exam. No backlogs. Freshers from batch 2026 or 2027 are encouraged to apply. Candidates with internship or project experience in data analytics or AI are preferred.

1
Round 1: Technical interview (SQL, Python, Data Analysis)
2
Round 2: Technical interview (AI/ML, Tableau/Power BI)
3
Round 3: HR interview
Mastercard is a global technology leader in the payments industry, committed to powering an inclusive digital economy. With operations in over 200 countries, the company offers a range of secure payment solutions and innovative financial services. The organization places a strong emphasis on data‑driven decision making, leveraging advanced analytics and AI to deliver value to customers and partners alike. In India, Mastercard has a growing presence, especially in Pune, where it supports a diverse workforce and fosters a culture of collaboration, continuous learning, and innovation. The Analyst, Big Data Analytics & Engineering role is a techno‑functional position that blends technical expertise with business acumen. As an analyst, you will design and develop tools that quantify the value of Mastercard’s services during the pre‑sales process. You will work closely with Sales, Marketing, Consulting, and Product teams to translate complex data sets into actionable insights that help internal stakeholders articulate ROI to potential clients. Your responsibilities include managing large data repositories, writing efficient SQL queries, performing statistical analysis, and building interactive dashboards in Tableau or Power BI. You will also apply machine learning techniques to enhance pre‑sales tools, enabling data‑driven recommendations that improve customer engagement and drive business outcomes. Key responsibilities include: 1) Developing value‑quantification models and ROI calculators; 2) Extracting and cleaning data from relational databases; 3) Performing trend analysis and correlation studies; 4) Building and maintaining dashboards; 5) Integrating AI/ML algorithms; 6) Collaborating with cross‑functional teams; 7) Presenting findings to senior leadership; 8) Optimizing processes for efficiency; 9) Ensuring data security and compliance; 10) Staying updated on industry best practices. The tech stack you will work with includes SQL, Python, R, Tableau, Power BI, Excel, and cloud data platforms. Growth opportunities are abundant: from analyst to senior analyst, data scientist, or product manager roles. Mastercard’s culture encourages experimentation, values diversity, and rewards high performance. Employees enjoy a healthy work‑life balance, flexible working arrangements, and access to global learning resources. If you are passionate about turning data into business value and want to work in a dynamic, inclusive environment, this role offers a unique chance to shape the future of internal operations at one of the world’s leading payment companies.

Mastercard 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 🎯
Mastercard Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Mastercard 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, R, Tableau, Power BI, Excel, Data Analysis, AI/ML, Statistical Analysis, Database Management, Business Intelligence, Communication, Presentation, Cross-functional Collaboration) & 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
Mastercard 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 Mastercard as a Analyst, Big Data Analytics & Engineering?
Preparation Tip: Highlight Mastercard'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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