Amazon Data Engineer Interview Guide
Amazon data engineering interviews combine technical depth with Leadership Principle evaluation in every round. SQL is tested in every loop. LP stories determine whether a strong technical candidate gets the offer.
Leadership Principles That Matter Most for DEs
Amazon has 16 Leadership Principles, but DE interviews consistently test these 5. Every behavioral answer should explicitly connect to at least one principle.
Customer Obsession
Data engineers serve internal customers: analysts, data scientists, product managers. Amazon wants to hear how you prioritized their needs, understood their pain points, and delivered data products that solved real problems. Every behavioral answer should connect back to the person who used your work.
Ownership
You built it, you own it. Amazon expects data engineers to monitor their pipelines, respond to failures, and improve reliability without being asked. Stories about taking end-to-end responsibility for a data system, including the parts that were not your formal job, land hard with interviewers.
Dive Deep
When a pipeline breaks, do you look at the error message and restart it, or do you investigate the root cause? Amazon wants engineers who dig into the data, question anomalies, and understand their systems at a granular level. Bring stories about finding subtle bugs that others missed.
Bias for Action
Speed matters at Amazon. They want engineers who make decisions with 70% of the information rather than waiting for 100%. Share examples where you shipped a V1 quickly, gathered feedback, and iterated. Analysis paralysis is a red flag in Amazon interviews.
Earn Trust
Trust comes from delivering reliably and communicating honestly. Amazon interviewers look for candidates who admit mistakes, share credit, and are transparent about tradeoffs. If your pipeline had a data quality issue, how you communicated it matters as much as how you fixed it.
The Amazon DE Interview Loop
The loop runs 5 to 6 stages. Onsite is one full day, usually four or five back-to-back rounds. Every round holds back 10 to 15 minutes for behavioral questions.
- 01
Online Assessment (OA)
Many Amazon DE roles start with an online assessment. This includes 1 to 2 SQL problems and sometimes a Python coding problem, completed on a proctored platform. The SQL questions test aggregation, joins, and window functions on Amazon-like schemas (orders, shipments, inventory, customer reviews). The difficulty is moderate, but you are timed, and there is no partial credit. Some roles skip the OA entirely and go straight to the phone screen.
- ▸Practice timed SQL problems. The OA gives you roughly 30 minutes per SQL question
- ▸Read the problem statement twice. Amazon OA questions often have subtle constraints buried in the description
- ▸If there is a Python component, expect data manipulation (parsing, transforming dictionaries, file processing), not algorithms
- ▸Test your solution against the provided examples, then think about edge cases before submitting
- 02
Phone Screen
A video call with a data engineer from the hiring team. The format is typically 30 to 35 minutes of technical questions (SQL and possibly Python) followed by 10 to 15 minutes of behavioral questions tied to Leadership Principles. The technical portion is harder than the OA. Expect multi-step SQL problems involving window functions, self-joins, and date arithmetic. The interviewer will ask you to explain your approach before you write code.
- ▸Explain your approach before writing SQL. Amazon interviewers assess your thinking, not just the final query
- ▸For behavioral questions, use the STAR format and name the Leadership Principle your answer demonstrates
- ▸If the interviewer asks 'What would you do differently next time?', they are testing self-awareness, not criticism
- ▸Prepare for questions about data quality. Amazon cares deeply about data accuracy because it affects customer experience
- 03
Onsite: SQL Deep Dive
The most technically demanding SQL round in the loop. Two to three problems with increasing difficulty, often set in Amazon contexts (order fulfillment, inventory tracking, seller performance, delivery estimates). The interviewer expects you to write clean, efficient SQL and discuss optimization. After solving a problem, you may be asked: 'This table has 10 billion rows. How would you make this query fast?'
- ▸Amazon schemas often include timestamps, status columns, and hierarchical categories. Practice queries involving time-based aggregation and status transitions
- ▸When discussing optimization, mention partitioning by date, indexing on join columns, and avoiding SELECT * on wide tables
- ▸The interviewer may ask you to rewrite a correlated subquery as a join or vice versa. Know both approaches
- 04
Onsite: System Design / Pipeline Architecture
Design a data pipeline or data platform component for an Amazon use case. Common prompts: real-time order tracking analytics, seller performance monitoring, recommendation engine data pipeline, or inventory forecasting data platform. You are expected to drive the conversation, sketch architecture, estimate data volumes, and discuss monitoring and alerting.
- ▸Start by clarifying requirements: latency SLA, data volume, consumers, and what 'correct' means for this use case
- ▸Amazon loves operational excellence. Include monitoring, alerting, runbooks, and auto-recovery in your design
- ▸Mention AWS services where appropriate (Kinesis, Redshift, Glue, S3, Lambda) but explain why you chose them over alternatives
- 05
Onsite: Behavioral / Leadership Principles
A full round dedicated to behavioral questions, each mapped to specific Leadership Principles. The interviewer will explicitly ask about situations that demonstrate Customer Obsession, Ownership, Dive Deep, Bias for Action, and Earn Trust. Some interviewers cover 3 to 4 principles in one round, asking follow-up questions that probe the depth and authenticity of your examples.
- ▸Prepare 2 stories per Leadership Principle. You need backups in case one story does not fit the specific question
- ▸Quantify every result: latency reduction, cost savings, pipeline uptime, data freshness improvement
- ▸Be honest about failures. Amazon values 'Earn Trust,' and admitting a mistake (with lessons learned) is stronger than pretending everything went perfectly
- 06
Onsite: Bar Raiser
The Bar Raiser is a specially trained interviewer from outside the hiring team. Their job is to evaluate whether you raise the bar for Amazon overall, not just whether you can do this specific job. The Bar Raiser's round is a mix of technical and behavioral questions, and they have the authority to veto a hire even if all other interviewers say yes.
- ▸Treat this round with the same preparation as any other. The Bar Raiser is more experienced at detecting rehearsed or inflated answers
- ▸The Bar Raiser often asks 'Why?' multiple times to test depth. Have genuine understanding behind every claim in your resume
- ▸If the Bar Raiser pivots to a topic you did not expect, stay calm and think out loud. They are testing adaptability as much as knowledge
5 Real-Style Amazon DE Interview Questions
These reflect the style, domain context, and difficulty of actual Amazon DE interviews.
For each product category, find the seller whose orders were delivered late most frequently in the last 90 days. Include the late delivery count and percentage.
Join orders to deliveries, filter to the last 90 days, flag late deliveries (actual_delivery_date > promised_delivery_date). Group by category and seller_id, count late deliveries. Use ROW_NUMBER() OVER (PARTITION BY category ORDER BY late_count DESC) to find the top seller per category. Calculate percentage as late_count divided by total_count.
Write a query that identifies customers whose monthly spend increased for 3 consecutive months.
Aggregate orders to monthly spend per customer. Use LAG to compare each month to the previous month. Flag months where spend increased. Then use the consecutive-group technique (ROW_NUMBER minus month_number) to find streaks. Filter for streaks of length 3 or more. Handle months with no orders by treating them as zero spend or skipping them.
Design a real-time pipeline that detects and flags potentially fraudulent seller listings within 5 minutes of creation.
Ingest new listing events from a Kinesis stream. A Flink or Spark Streaming job applies rule-based checks (price anomalies, keyword patterns, seller history) and ML model scores in real time. Flagged listings go to a review queue and are hidden from search results until reviewed. Store raw events in S3 for model retraining. Discuss the tradeoff between false positives and false negatives. Address how the system handles spikes during Prime Day.
Tell me about a time you took ownership of a data quality issue that was not technically your responsibility.
Use STAR format. Describe a situation where a downstream team reported incorrect numbers, the source was an upstream pipeline owned by another team. Explain how you dug in (Dive Deep), identified the root cause, built a fix or workaround, and coordinated with the owning team. Quantify the impact. Show that you did not wait for someone else to fix it (Ownership) and communicated transparently about the scope of the issue (Earn Trust).
Write a function that processes a stream of order events and detects duplicate orders. Two orders are duplicates if they have the same customer_id, product_id, and were placed within 60 seconds of each other.
Maintain a dictionary keyed by (customer_id, product_id) with the most recent order timestamp as the value. For each incoming event, check if the key exists and whether the time difference is under 60 seconds. If so, flag as duplicate. Handle edge cases: out-of-order events, the dictionary growing unbounded (implement TTL or periodic cleanup).
Preparation Strategy
How to allocate your prep time for an Amazon DE loop.
Master Amazon SQL patterns
Amazon SQL questions often involve e-commerce schemas: orders, products, sellers, shipments, returns, and reviews. Practice queries involving time-based filtering (last 90 days, month-over-month comparisons), status transitions (ordered to shipped to delivered), and ranking (top sellers, most returned products). Do 3 to 5 timed problems per day for 2 weeks.
Map your stories to Leadership Principles
Create a matrix: Leadership Principles on one axis, your career stories on the other. Each story should map to 2 to 3 principles. Write out STAR bullets for each story. Practice telling them out loud in under 3 minutes. Amazon behavioral prep takes as much time as technical prep, and most candidates under-invest here.
Practice system design with AWS services
Amazon interviewers expect familiarity with AWS. Saying 'I would use Kinesis for streaming ingestion, S3 for raw storage, Glue for ETL, and Redshift for the warehouse' is much more credible than generic answers. Study 3 to 4 common DE system design problems and practice sketching architecture with AWS components.
Simulate the full loop
An Amazon onsite is 4 to 5 back-to-back rounds over a full day. Stamina matters. Do at least one full mock loop: 4 rounds in a row with 5-minute breaks between them. Notice when your energy drops and your answers get vague. That is the round you need to prepare more for.
Amazon DE Interview FAQ
How many rounds are in an Amazon DE onsite?+
What are the most important Leadership Principles for DE roles?+
Does Amazon use LeetCode-style algorithm questions for DEs?+
What is the Bar Raiser, and should I be worried about it?+
Twelve STAR Stories and a Clean Window Function
- 01
Active recall beats re-reading by 50%
Cognitive-science meta-reviews (Dunlosky et al., 2013) rank practice testing as a top-tier study technique, while re-reading and highlighting rank near the bottom
- 02
76% of hiring managers reject on the coding task, not the resume
From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice
- 03
Five problem shapes cover 80% of data engineer loops
Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition