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REQUIREMENT_ID_1522 β€’ 3-DAY_ACTIVE_POLICY

Business Graduate Associate – Data & Analytics

Company
Company London Stock Exchange Group (LSEG)
Type
Opportunity Type Internship
Salary
Stipend / Salary 6 - 11 LPA
Location
Location Bengaluru, Karnataka
Posted Date
Posted Date Today
SQL Python Data Analytics Artificial Intelligence Business Intelligence Product Management Financial Markets Business Analytics Problem Solving Strategic Thinking Customer Understanding Communication Collaboration
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Aptitude and Logical Reasoning Practice Open Resource β†—
Helps sharpen analytical thinking and problem‑solving skills, essential for data‑driven roles at LSEG.
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Technical Interview Preparation Open Resource β†—
Provides coding and data structure practice to prepare for SQL and Python rounds.
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Product Management Fundamentals Open Resource β†—
Covers product lifecycle and customer‑centric thinking, useful for LSEG’s product‑led approach.
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Algorithmic Challenges Open Resource β†—
Enhances problem‑solving and coding speed for technical interviews.

Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Data Analytics, Business Analytics, Engineering, or related quantitative disciplines. Must have completed studies in 2026 or be a final‑year student expected to graduate before summer 2027. Minimum aggregate of 60% (or equivalent CGPA). No backlogs allowed. Preferred candidates have a combination of Engineering and Finance background and foundational knowledge of SQL, Python, and financial markets.

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Round 1: Technical – SQL & Python coding + data analytics questions
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Round 2: Technical – case study on product/market scenario
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Round 3: HR – cultural fit & motivation
London Stock Exchange Group (LSEG) is a leading global financial markets infrastructure provider that powers the world’s capital markets. With a history spanning over a century, LSEG offers a suite of data, analytics, and technology solutions that enable market participants to trade, settle, and analyze financial instruments with speed and confidence. The company’s mission is to make financial markets more efficient, transparent and accessible, and it achieves this through a combination of cutting‑edge technology, deep market expertise and a customer‑centric product approach. The Business Graduate Associate – Data & Analytics programme is a 12‑month rotational learning experience designed for recent graduates who are passionate about data, technology and finance. Based at Divyasree Technopolis in Bengaluru, the role immerses you in LSEG’s Data & Analytics division, where you will work on real‑world projects that span data engineering, business intelligence, AI, and product management. You will collaborate with cross‑functional teams, including product managers, data scientists, and market experts, to transform raw data into actionable insights that drive product decisions and customer value. Key responsibilities include: 1. Designing and executing SQL queries to extract and transform data from LSEG’s data warehouses. 2. Developing simple Python scripts for data cleaning, analysis, and visualization. 3. Assisting in the creation of dashboards and reports for internal stakeholders. 4. Participating in product discovery sessions to understand customer pain points. 5. Contributing to AI/ML model prototypes that enhance market data services. 6. Conducting market research to support product roadmap decisions. 7. Collaborating with engineering teams to implement data pipelines. 8. Presenting analytical findings to senior leadership. 9. Supporting the rollout of new data products and features. 10. Continuously learning about financial markets, asset classes, and corporate finance. The tech stack you’ll encounter includes SQL, Python, Tableau/Power BI, and cloud platforms such as AWS or Azure. Throughout the programme, you will gain exposure to product management principles, agile development practices, and financial market fundamentals. Career growth: Graduates who excel are offered permanent roles within LSEG’s Data & Analytics division, with opportunities to progress into senior analyst, product manager, or data science roles. The programme also provides a clear pathway to global assignments and leadership tracks. Why join LSEG? You’ll be part of a global organization that shapes the future of financial markets, work alongside industry experts, and receive mentorship from senior leaders. The blend of technical, analytical, and business challenges ensures a dynamic learning environment that prepares you for a long‑term career in data‑driven finance.

London Stock Exchange Group (LSEG) 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 🎯
London Stock Exchange Group (LSEG) Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for London Stock Exchange Group (LSEG) 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, Data Analytics, Artificial Intelligence, Business Intelligence, Product Management, Financial Markets, Business Analytics, Problem Solving, Strategic Thinking, Customer Understanding, Communication, 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
London Stock Exchange Group (LSEG) 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 London Stock Exchange Group (LSEG) as a Business Graduate Associate – Data & Analytics?
Preparation Tip: Highlight London Stock Exchange Group (LSEG)'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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