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REQUIREMENT_ID_1383 β€’ 3-DAY_ACTIVE_POLICY

Central Operations Support Executive, IN External Fulfillment

Company
Company Amazon
Type
Opportunity Type Internship
Salary
Stipend / Salary β‚Ή12 LPA
Location
Location Karnataka, India
Posted Date
Posted Date Sep 28, 2026
Excel SQL data analysis stakeholder management communication problem solving attention to detail process documentation
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Amazon Placement Papers – Practice Questions Open Resource β†—
Curated set of past Amazon placement papers to help you practice quantitative and logical reasoning required for the online assessment.
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Amazon Recruitment Process – Interview Experiences Open Resource β†—
First‑hand accounts of candidates who cleared Amazon interviews, offering insights into question patterns and preparation strategies.
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Amazon Interview Preparation Guide Open Resource β†—
Comprehensive guide covering Amazon’s leadership principles, typical case studies, and tips to ace each interview round.
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Algorithm & Data Structure Problem Set Open Resource β†—
Extensive collection of coding problems to sharpen problem‑solving skills, useful for technical rounds that test SQL and analytical thinking.

Bachelor's degree in Computer Science, Engineering, Statistics or any quantitative/technical discipline; minimum 60% aggregate or 6.5 CGPA; no active backlogs at the time of joining; graduating batch 2022‑2025 (flexible for freshers); strong analytical and communication skills.

1
Round 1: Online assessment (aptitude & logical reasoning)
2
Round 2: Technical interview (Excel/SQL case study)
3
Round 3: Managerial interview (Amazon Leadership Principles)
4
Round 4: HR round (fit & compensation discussion)
Amazon, founded in 1994, has grown into one of the world’s most customer‑centric companies, operating a vast ecosystem that spans e‑commerce, cloud computing, digital streaming, and logistics. In India, Amazon’s fulfillment network is a critical engine that connects millions of sellers to customers across the country. The company invests heavily in technology, data‑driven decision making, and a culture of relentless innovation, offering fresh graduates a platform to work on high‑impact problems at scale. The Central Operations Support Executive (COSE) role is a gateway for fresh talent to dive into Amazon’s external fulfillment operations. Reporting to the Central Ops team, the COSE remotely monitors seller performance, coordinates with field executives, and ensures smooth inbound and outbound logistics. The position blends data analysis, stakeholder management, and process improvement, demanding a keen eye for patterns, a proactive ownership mindset, and strong communication skills. Key responsibilities include: 1. Monitor daily metrics of assigned seller sites through outbound calls, emails, and dashboards. 2. Identify anomalies or disruptions in seller operations and trigger timely field support. 3. Act as a liaison between sellers, field executives, and internal Amazon teams to ensure seamless execution. 4. Conduct virtual training sessions for sellers on operational norms and best practices. 5. Develop, document, and roll out Standard Operating Procedures (SOPs) for recurring issues. 6. Analyse performance data using Excel and, where applicable, SQL to surface insights. 7. Propose feature enhancements or process changes that improve seller experience. 8. Participate in periodic audits to verify compliance with Amazon policies. 9. Support the central control centre by escalating critical incidents to senior leadership. 10. Contribute to cross‑functional projects aimed at scaling fulfillment capabilities. Tech stack primarily involves advanced Excel (pivot tables, macros), basic SQL for data extraction, and Amazon’s internal reporting tools. Familiarity with data visualisation (e.g., Tableau) is a plus. The role offers a clear growth trajectory: high performers can move into senior operations analyst positions, then into program management or regional operations leadership, eventually leading large seller‑centric teams. Why join Amazon? Freshers get exposure to a global supply‑chain operation, mentorship from seasoned Amazonians, and a compensation package that includes competitive salary, stock units, and comprehensive benefits. The fast‑paced environment nurtures problem‑solving abilities, and the company’s commitment to diversity and inclusion ensures a supportive workplace for all.

Amazon 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 🎯
Amazon Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Amazon 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 (Excel, SQL, data analysis, stakeholder management, communication, problem solving, attention to detail, process documentation) & 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
Amazon 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 Amazon as a Central Operations Support Executive, IN External Fulfillment?
Preparation Tip: Highlight Amazon's market reputation, recent tech innovations, and how your skills in Excel 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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