Amazon Data Engineer Interview (2026)
Amazon's DE interview is unique because Leadership Principles carry as much weight as technical skills. Every round blends behavioral questions with SQL or system design. The technical side covers SQL, system design with AWS services, and sometimes Python. Below: the process, the five LPs that matter most for DEs, and 10 example questions with approaches.
Interview Process
Three stages. The onsite loop is where most evaluation happens.
- 01
Recruiter Screen
Mostly logistics and background review. The recruiter confirms your experience level, asks about interest in Amazon, and explains the process. They may ask one or two Leadership Principle questions early. Have a concise pitch about your data engineering experience and why Amazon.
- ▸Know which team you are interviewing for; Amazon has hundreds of DE teams across Retail, AWS, Alexa, Ads, Prime Video
- ▸Mention AWS services experience if you have it; not required but helps
- ▸Ask about the team's data stack and the problems they solve
- 02
Technical Phone Screen
Split between SQL/coding and Leadership Principle questions. Expect 1 to 2 SQL problems and 1 to 2 behavioral questions. Amazon phone screens run 60 minutes, longer than most companies. The SQL is intermediate difficulty. Behavioral questions carry real weight even in the phone screen.
- ▸Every answer should map to a Leadership Principle
- ▸For SQL, expect aggregation, JOINs, and basic window functions
- ▸Use STAR format and quantify impact with specific numbers
- 03
Onsite Loop (4 to 5 rounds)
Four to five interviewers each test a different combination of technical skills and Leadership Principles. Every round includes at least one behavioral question. There is no purely behavioral round; LPs are woven into every interview. Technical rounds cover SQL, system design, and sometimes Python. One interviewer is the 'bar raiser' with veto power.
- ▸Each interviewer is assigned specific LPs to evaluate
- ▸Prepare at least 2 stories per principle for Dive Deep, Ownership, Bias for Action
- ▸The bar raiser asks the hardest questions and holds the highest standard
Leadership Principles for Data Engineers
Amazon has 16 LPs. These five are most relevant for DE roles and most frequently tested.
Dive Deep
The most relevant LP for data engineers. Amazon wants DEs who dig into data quality issues, trace anomalies to root causes, and refuse surface-level explanations. When a dashboard number looks wrong, you investigate the pipeline, source data, and transformation logic before reporting the issue.
Ownership
DEs who own their pipelines end-to-end. You do not throw data over the wall. You monitor, alert on failures, and fix issues proactively. Amazon wants to hear about times you took ownership beyond your explicit responsibilities.
Bias for Action
Speed matters at Amazon. They want DEs who make decisions with incomplete information and iterate. If a stakeholder needs a dataset and the perfect solution takes 3 months, what can you deliver in 2 weeks? The interim solution demonstrates Bias for Action.
Insist on the Highest Standards
Data accuracy is non-negotiable. Amazon runs on data for pricing, inventory, recommendations, and logistics. A wrong number can cost millions. This LP tests whether you build validation, testing, and monitoring into your work.
Learn and Be Curious
Data engineering tools change fast. Amazon wants DEs who keep up with new tools, evaluate them critically, and adopt what works. This LP comes up when discussing how you chose a technology or learned a new domain.
10 Example Questions
Technical and behavioral questions from an Amazon DE loop.
Given orders and returns tables, find the top 10 customers by net revenue (total orders minus total returns).
LEFT JOIN orders to returns on customer_id, aggregate both, subtract. COALESCE returns to 0 for customers with no returns. Tests JOIN type selection and NULL handling.
Find products whose daily sales dropped more than 50% compared to the previous day.
LAG(daily_sales) OVER (PARTITION BY product_id ORDER BY date) for previous day, filter where current < 0.5 * previous. Tests window functions and computed column filtering.
Show cumulative percentage of total revenue by product category, ordered by revenue descending.
SUM(revenue) per category, then SUM(cat_revenue) OVER (ORDER BY cat_revenue DESC ROWS UNBOUNDED PRECEDING) / total * 100. Tests aggregation, window functions, arithmetic.
Design a real-time inventory tracking system for Amazon warehouses.
Event-driven: inventory change events to Kinesis, Flink for processing, DynamoDB for real-time state, S3 + Glue for batch analytics. Discuss consistency, scale (millions of items, hundreds of warehouses), and real-time vs query performance tradeoffs.
Design a pipeline to compute product recommendations from purchase history.
Batch: daily ETL from orders, build co-purchase matrices, store in Redshift or S3. Serving: precomputed recs in DynamoDB. Discuss cold start, data freshness, and A/B testing the model.
How would you build a data quality framework for 10,000+ tables?
Prioritize by business impact. Automated checks: schema drift, row count anomalies, NULL rate monitoring, freshness SLAs. Alerting tiers: PagerDuty for critical, email for informational. Discuss false positive management.
Tell me about a time a pipeline failed in production. (Dive Deep)
STAR format. Describe what broke, how you diagnosed (logs, lineage, data profiling), root cause, fix, and prevention. Quantify: 'Affected 2.3M rows, identified root cause in 45 minutes, deployed fix in 2 hours, added 3 automated checks.'
Describe a time you disagreed with a stakeholder about data requirements. (Ownership)
Show constructive pushback. You understood their goal, explained technical constraints, proposed an alternative that met 80% of the need in 20% of the time. The stakeholder accepted because you solved their problem.
Tell me about delivering something imperfect because speed mattered. (Bias for Action)
Show deliberate tradeoff. Shipped V1 with 3 of 5 metrics, documented gaps, committed to timeline for the rest. Stakeholder started making decisions immediately instead of waiting 6 weeks.
How do you stay current with new data engineering tools? (Learn and Be Curious)
Be specific: name tools evaluated recently (dbt, Dagster, DuckDB, Iceberg). Explain evaluation criteria: does it solve a real problem better, what is migration cost, is the community active?
Problems sourced from real Amazon interview reports. Run your code in the browser.
AWS Services to Know
Familiarity with these shows you understand the AWS data ecosystem.
Redshift
Columnar warehouse. Know distribution keys, sort keys, and how they affect query performance. Interviewers may ask when to use Redshift vs Athena.
S3
Object storage backbone. Know partitioning strategies (by date, by source) and file formats (Parquet for analytics, JSON for raw ingestion).
Glue
Managed ETL (serverless Spark). Glue Crawlers discover schemas; Glue Data Catalog is a Hive metastore. Know Glue vs EMR tradeoffs.
EMR
Managed Spark/Hadoop. For heavy batch processing. Know cost model: transient clusters for batch vs persistent for interactive.
Kinesis
Real-time streaming. Data Streams for ingestion, Firehose for delivery, Data Analytics for processing. Know how it compares to Kafka.
Athena
Serverless SQL on S3. No infrastructure. Pay per query. Best for ad-hoc analysis when data is already in S3.
Amazon DE Interview FAQ
How important are Leadership Principles at Amazon?+
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Prepare for Amazon's Dual Bar
Amazon evaluates technical skills and Leadership Principles with equal weight. Practice SQL at interview difficulty while preparing your LP stories.