Hi community, I wanted to share my experience for the 2 roles that I interviewed for at Amazon.

SDE Intern:

Timeline:
applied - Jan 31st
OA - Feb 1st week
VO - March 2nd week
Waitlisted - March 3rd week

Interview experience:
My interview was not like the usual ones. After the introductions, the interviewer set the definition of the interview, saying that they will ask only 1 coding question, and we will go over the approach and solution. So I wasn't asked any LP in this one.

The coding question was about printing node values in a certain order, in a Binary Tree. It took me about 40-45 mins to solve it. I got the initial approach in 5 mins, and started talking about how I would go about it, wrote some pseudocode, and explained why, with a dry run. The Interviewer gave an edge case where this would fail, and I immediately got a better approach in my mind. I explained that and wrote the code quickly, and the interviewer went through code and was satisfied. I asked him questions for the last 10 mins.

My prep:
2 weeks of non-stop leetcode grind (Blind75 + some new problems in NeetCode150) and prepping behavioral questions by writing stories that mapped to multiple LPs. Having 4-5 stories mapped to a few LPs each will be fine. I had followed the STAR format as mentioned in Amazon's prep materials.

Data Science Intern:

Timeline:
Applied - not sure, probably Dec-Feb sometime
VO - March 3rd Week
Decision - 3rd day after VO

Interview experience:
I had 2 rounds back to back on the same day. I was interviewing with the team that would hire me. The first round was completely about LP. That's 1hr of LP. The 2nd round covered things about my resume, end-to-end workflow of one of my most complex projects, some ML theory and fundamentals, follow-ups about the project I explained, 3 SQL queries (1 + 2 follow-up), 1 simple coding question, and finally 2 LP questions.

The ML theory was just fundamentals; If you read and study daily, it will help you retain your knowledge. The fun part was the end-to-end project discussion. I was completely involved in explaining things, linking the business aspects and value with technical aspects and value, and how data science helped solve a real-world issue.

My prep:
For SQL, I just practiced SQL 50 on leetcode every day. I already had a good grip on SQL given my previous semester's coursework, so it wasn't a problem. I didn't touch leetcode for DSA and LP because, well, I had already prepped for SDE VO. I read a few books for ML theory, and wrote down notes about my projects (work ex. and personal projects), connected all dots, and wrote deep notes for everything, and read them once a day.

Finally, on the 3rd day after my DS VO, I got an email from a recruiter thanking me for interviewing for both roles, and that the team wanted to move ahead with the DS role. I happily accepted it, as DS was my top choice :)

LP prep materials:
https://assets.aboutamazon.com/d4/9b/6d5662ec4a75961ae78c473e7d03/amazon-leadership-principles-070621-us.pdf
https://igotanoffer.com/blogs/tech/amazon-behavioral-interview

ML prep:
Just a lot of Google searching and reading blogs every day

Feel free to ask me any questions, I'll try to answer them!

I recently received an interview invitation for the Data Engineer Summer Internship at Amazon US, and I’m feeling a bit overwhelmed with how to best prepare. I’m confused about what topics to focus on, what types of questions to expect, and any general tips for tackling the interview process. Has anyone been through this interview before, or does anyone have advice on study materials, preparation strategies, or any insider insights on the process?

For context, here’s the complete job description I received:

By applying to this position, your application will be considered for all Data Engineer roles at all locations we hire for in the United States including but not limited to: Greater Seattle Area (Seattle, Bellevue, Redmond), Greater Bay Area (San Francisco, Sunnyvale, Santa Clara), Greater DMV (DC, MD, VA), Austin (TX), New York City (NY), Minneapolis (MN).

You will be able to provide your preference of location and start date during the application process but, we cannot guarantee that we can meet your selection based on several factors including but not limited to the availability and business needs of this role. Finalization on the location and start dates available will be provided to you at the time of job offer.

Start dates for our internships in this posting include the following period:

Summer (Starts May/June 2025)

Do you love building data pipelines? Are you excited by the opportunity to design tools and infrastructure needed to analyze large volumes of data? Do you want to help solve big data warehousing problems, and partner with stakeholders to understand how to best design and implement cutting edge data solutions that provide answers to key business questions? Do you want to be a part of a fast-paced environment and contribute to one of the most visited sites on the Internet?

If this describes you, consider joining us as an Amazon is looking for a data engineer intern to join one our many lines of business. Amazon interns have the opportunity to work alongside the industry’s brightest engineers who innovate every day on behalf of our customers. You will be matched to a manager and a mentor. You will have the opportunity to impact the evolution of Amazon technology as well as lead mission critical projects early in your career. Your work will contribute to solving some of the most complex technical challenges in the company.

Please review the following page for information on Amazon University Talent's recruiting timeline and additional FAQs: https://techengamazon.splashthat.com/.

Key job responsibilities

As a Data Engineer intern, you will/may:

Design, implement, and automate deployment of our distributed system for collecting and processing log events from multiple sources.

Design data schema and operate internal data warehouses and SQL/NoSQL database systems.

Own the design, development, and maintenance of ongoing metrics, reports, analyses, and dashboards that engineers, analysts, and data scientists use to drive key business decisions.

Monitor and troubleshoot operational or data issues in the data pipelines.

Drive architectural plans and implementation for future data storage, reporting, and analytic solutions.

Develop code based automated data pipelines able to process millions of data points.

Improve database and data warehouse performance by tuning inefficient queries.

Work collaboratively with Business Analysts, Data Scientists, and other internal partners to identify opportunities/problems.

Provide assistance with troubleshooting, researching the root cause, and thoroughly resolving defects in the event of a problem.

A day in the life

In addition to working on an impactful project, you will have the opportunity to engage with Amazonians for both personal and professional development, expand your network, and participate in fun activities with other interns throughout the summer. No matter the location of your internship, we give you the tools to own your summer and learn in a real-world setting.

We’re on the lookout for the curious, those who think big and want to define the world of tomorrow.

At Amazon, you will grow into the high impact, visionary person you know you’re ready to be. Every day will be filled with exciting new challenges, developing new skills, and achieving personal growth.

How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow.

Basic Qualifications

Experience with database, data warehouse or data lake solutions

Experience with SQL

Experience with one or more scripting language (e.g., Python, KornShell, Scala)

Are 18 years of age or older

Work 40 hours/week minimum and commit to 12 week internship maximum

Experience with data transformation.

Currently enrolled in or will receive a Bachelor’s in Computer Science, Computer Engineering, Information Management, Information Systems, or an equivalent technical discipline with a conferral date between October 2025 – December 2028.

Preferred Qualifications

Knowledge of basics of designing and implementing a data schema like normalization, relational model vs dimensional model

Experience building data pipelines or automated ETL processes

Experience writing and optimizing SQL queries with large-scale, complex datasets

Experience with big data processing technology (e.g., Hadoop or ApacheSpark), data warehouse technical architecture, infrastructure components, ETL, and reporting/analytic tools and environments

Experience with data visualization software (e.g., AWS QuickSight or Tableau) or open-source project

Enrolled in a Master’s Degree or advanced technical degree with a conferral date between October 2025 – December 2028.

Previous technical internship(s), if applicable

Prior experience with AWS

Can articulate the basic differences between datatypes (e.g. JSON/NoSQL, relational)

Given the extensive nature of the role and responsibilities, I’m unsure where to begin. Should I focus more on building data pipelines, brushing up on SQL and database design, or dive deeper into big data processing frameworks like Apache Spark? Also, if anyone has specific tips for interviews at Amazon, especially for Seattle-based roles, please share your insights and any resources that might have helped you.

Thanks in advance for your help!

Looking forward to your advice and experiences.