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REQUIREMENT_ID_339 β€’ 3-DAY_ACTIVE_POLICY

Data Engineer I

Company
Company Amazon
Type
Opportunity Type Internship
Salary
Stipend / Salary 12-15 LPA
Location
Location Bengaluru
Posted Date
Posted Date Today
SQL ETL AWS Big Data (Spark/Hive) Python Data Modeling Warehousing Hadoop EMR Glue Athena S3 scripting (Python KornShell) query optimization
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Amazon Placement Papers Open Resource β†—
Provides sample questions and case studies that reflect the type of data engineering problems Amazon may pose during interviews.
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Amazon Recruitment Process Insights Open Resource β†—
Offers a detailed walkthrough of Amazon’s interview stages, common questions, and tips for preparation.
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Amazon Interview Preparation Guide Open Resource β†—
Contains curated resources, practice problems, and interview strategies tailored for Amazon roles.
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Coding Practice Problems Open Resource β†—
A collection of algorithmic challenges to sharpen coding skills, essential for Amazon’s technical interviews.

Bachelor’s degree (B.E/B.Tech/B.Sc) in Computer Science, Information Technology, or related field. Minimum 60% in final year or equivalent. 1+ years of data engineering experience. No backlogs in the final year. Strong academic record and relevant project experience are preferred.

1
Round 1: Technical (Coding + System Design)
2
Round 2: Technical (Advanced)
3
Round 3: HR
Amazon is one of the world’s leading e‑commerce and cloud computing giants, known for its relentless focus on customer obsession, innovation, and operational excellence. With a presence in over 200 countries, Amazon’s Indian operations have grown rapidly, creating millions of jobs across technology, logistics, and services. The International Seller Services (ISS) Central Analytics team in Bengaluru is a key pillar that empowers sellers worldwide by providing data‑driven insights and tools to scale their businesses. As a Data Engineer I, you will join this high‑impact team and help shape the data infrastructure that fuels Amazon’s global marketplace. Role Summary The Data Engineer I role is designed for early‑career professionals who are passionate about building scalable data pipelines and working with large volumes of data. You will collaborate with data scientists, applied scientists, and economists to deliver reliable, high‑performance data solutions that support machine learning and AI initiatives. Your work will directly influence product decisions that affect millions of sellers and customers. Key Responsibilities 1. Design, develop, and maintain end‑to‑end ETL/ELT pipelines using AWS services. 2. Build and optimize data models for analytics and machine learning workloads. 3. Implement data ingestion routines from diverse sources (APIs, logs, third‑party feeds) into data lakes and warehouses. 4. Ensure data quality, integrity, and security across all stages of the pipeline. 5. Collaborate with cross‑functional teams to translate business requirements into technical specifications. 6. Monitor and troubleshoot production data pipelines for performance and reliability. 7. Automate data workflows using orchestration tools such as AWS Glue, Airflow, or Step Functions. 8. Stay current with emerging big‑data technologies and propose improvements. 9. Document data architecture, pipeline logic, and best practices. 10. Mentor junior engineers and share knowledge within the team. Tech Stack - AWS: S3, Glue, EMR, Redshift, Athena, Lambda, Step Functions - Big Data: Spark, Hive, Hadoop - Databases: Redshift, PostgreSQL, MySQL - Programming: Python, SQL, Bash/KornShell - Tools: Airflow, Git, Docker, CI/CD pipelines Growth Path Starting as a Data Engineer I, you can progress to Data Engineer II, Senior Data Engineer, or lead roles such as Data Engineering Manager or Principal Engineer. Amazon’s culture of continuous learning and internal mobility allows engineers to explore different domains, from cloud infrastructure to AI research. Why Join Amazon? Amazon offers competitive compensation, comprehensive benefits, and a culture that rewards innovation and ownership. Working at Amazon means being part of a global team that tackles complex problems, receives mentorship from industry leaders, and has access to cutting‑edge technologies. The opportunity to impact millions of users and sellers worldwide makes this role uniquely rewarding.

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 (SQL, ETL, AWS, Big Data (Spark/Hive), Python, Data Modeling, Warehousing, Hadoop, EMR, Glue, Athena, S3, scripting (Python, KornShell), query optimization) & 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 Data Engineer I?
Preparation Tip: Highlight Amazon'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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