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JD #2424
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Data Engineer Intern

Amazon

  • India (Remote / Multiple), India
  • Rs 30,000 per month
  • Internship
  • Posted Today
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About the role

Amazon is one of the world’s most customer‑obsessed companies, operating at a massive scale across e‑commerce, cloud computing, digital streaming, and AI‑driven services. In India, Amazon has built a robust ecosystem of fulfillment centers, data centers, and technology hubs in cities like Bengaluru, Hyderabad, Chennai, and Noida, offering fresh talent the chance to work on products that impact millions of users daily. The company’s culture emphasizes ownership, bias for action, and continuous learning, making it an ideal launchpad for engineering graduates who want to grow fast in a high‑velocity environment. The Data Engineer Intern role for 2027 is designed to give aspiring data professionals hands‑on experience building and maintaining large‑scale data pipelines that power business insights across Amazon’s diverse domains. Interns will collaborate with business analysts, software engineers, and senior data engineers to design, implement, and support analytical data platforms. The internship is full‑time and requires a commitment for the entire duration, with no academic or other work conflicts. Key responsibilities include: 1. Design, develop, and support analytical data platform solutions for data‑driven decision making. 2. Create and maintain data schemas, and manage internal data warehouses using SQL and NoSQL databases. 3. Build and optimise ETL pipelines that ingest data from varied sources using AWS services such as EMR, Redshift, and Elasticsearch. 4. Work with AWS technologies like S3, Lambda, Glue, and explore emerging services to improve efficiency. 5. Implement data‑lake components on Hadoop and Amazon S3, and run SQL‑on‑Hadoop workloads using Spark, Hive, and Impala. 6. Continuously improve analysis processes, simplifying self‑service data access for internal customers. 7. Produce and maintain metrics, dashboards, and reports that influence key business decisions. 8. Communicate findings and technical designs clearly to cross‑functional teams. 9. Follow best practices for data integrity, testing, documentation, and version control. 10. Participate in code reviews and agile ceremonies to deliver high‑quality solutions. Tech stack: Python, SQL, AWS (S3, Redshift, EMR, Lambda, Glue), Hadoop ecosystem (Spark, Hive, Impala), NoSQL databases, data‑lake architecture, ETL tools, and visualization platforms. Growth path: Successful interns often receive full‑time offers, progressing to Data Engineer, Data Platform Engineer, or Solutions Architect roles. Amazon’s internal mobility program allows movement across business units, enabling exposure to cutting‑edge technologies and leadership opportunities. Why join Amazon? The internship provides exposure to real‑world, petabyte‑scale data problems, mentorship from senior engineers, and a culture that rewards innovation. Interns gain a strong foundation in cloud‑native data engineering, positioning them for high‑impact careers in any data‑driven organization.

Eligibility & process

Who can apply

β€’ Currently enrolled in or have completed a Bachelor’s/Master’s degree in Computer Science, Information Technology, Electronics & Communication, or a related engineering discipline. β€’ Must be in the final year of study (graduating in 2027). β€’ Minimum CGPA/percentage of 60% (or equivalent). β€’ No active backlogs at the time of application. β€’ Strong written and verbal communication skills. β€’ Availability for a full‑time internship with no academic or other employment conflicts.

Skills & stack

Python SQL AWS S3 Redshift EMR Lambda Glue

Selection process

  1. Round 1: Online Assessment – coding problems focusing on data structures and algorithms
  2. Round 2: Technical Interview – deep dive into data engineering concepts, AWS services, and problem‑solving
  3. Round 3: HR Interview – discussion on fit, motivation, and internship logistics

Prep kit

20 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
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 (Python, SQL, AWS, S3, Redshift, EMR, Lambda, Glue) & 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.
Technical questions 5
What is the difference between WHERE and HAVING clauses in SQL? Answer
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
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
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
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
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.
Behavioural & HR questions 3
Why do you want to join Amazon as a Data Engineer Intern?
Highlight Amazon's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
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?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Amazon Rs 30,000 per month
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