Watched a senior DE torpedo a strong Amazon loop last cycle. SQL was clean, system design was solid, then the bar-raiser asked a behavioral and he told a story about shipping a pipeline on time. No failure. No metric. No LP. Rejection. Amazon doesn't care how good your code is if your stories don't land. Every round, technical or not, asks one question: which Leadership Principle did you live?
Leadership Principles
LPs that matter
Phone screens are SQL
Onsite rounds
Source: DataDriven analysis of 1,042 verified data engineering interview rounds.
Amazon has 16 Leadership Principles, but DE interviews consistently test these 5. Every behavioral answer should explicitly connect to at least one principle.
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.
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.
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.
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.
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 loop runs 5 to 6 stages. Onsite is one full day, usually four or five back-to-back rounds. Been through it twice. The pattern that breaks people: every round holds back 10 to 15 minutes for behavioral questions, and candidates blow through technical material so fast they have nothing left for the LP ambush at the end.
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.
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. The behavioral portion usually covers 1 to 2 Leadership Principles.
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?' The round ends with 5 to 10 minutes of behavioral questions.
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. Amazon interviews test whether you can reason about data at massive scale, handle failure gracefully, and make deliberate tradeoffs. You are expected to drive the conversation, sketch architecture, estimate data volumes, and discuss monitoring and alerting. The round includes behavioral questions about system design decisions you have made in past roles.
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. This is not a soft round. Amazon uses a structured rubric, and vague or generic answers result in a 'not inclined' rating.
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. The technical portion could be SQL, Python, or system design, depending on the Bar Raiser's background. The behavioral portion goes deep on 2 to 3 Leadership Principles.
These reflect the style, domain context, and difficulty of actual Amazon DE interviews.
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. The interviewer will ask about ties and whether you should use RANK instead of ROW_NUMBER.
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. The interviewer will probe how you handle months with no orders (do you treat them as zero spend or skip them?) and whether you use a date spine.
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 (blocking legitimate sellers) and false negatives (letting fraud through). Address how the system handles spikes during Prime Day.
Use STAR format. Describe the situation: 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: 'The incorrect data affected 12% of weekly reports for 3 weeks before I caught it.' Show that you did not wait for someone else to fix it (Ownership) and communicated transparently about the scope of the issue (Earn Trust).
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). The interviewer checks whether you think about memory management and what happens when this runs for days without restarting.
How to allocate your prep time for an Amazon DE loop.
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.
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.
Amazon interviewers expect familiarity with AWS. You do not need to be an expert, but 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.
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.
That's the minimum kit for an Amazon loop. Build both before the phone screen, or get downleveled by the bar-raiser.
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