Company Interview Guide
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.
Three stages. The onsite loop is where most evaluation happens.
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.
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.
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.
Amazon has 16 LPs. These five are most relevant for DE roles and most frequently tested.
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.
Example question: Tell me about a time you found a data quality issue that others had missed.
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.
Example question: Describe a situation where you went beyond your role to solve a data problem.
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.
Example question: Tell me about a time you had to make a quick decision with limited data.
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.
Example question: Describe how you maintain data quality in a pipeline you own.
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.
Example question: Tell me about a time you learned a new technology to solve a problem.
Technical and behavioral questions from an Amazon DE loop.
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.
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.
SUM(revenue) per category, then SUM(cat_revenue) OVER (ORDER BY cat_revenue DESC ROWS UNBOUNDED PRECEDING) / total * 100. Tests aggregation, window functions, arithmetic.
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.
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.
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.
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.'
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.
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.
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?
Familiarity with these shows you understand the AWS data ecosystem.
Columnar warehouse. Know distribution keys, sort keys, and how they affect query performance. Interviewers may ask when to use Redshift vs Athena.
Object storage backbone. Know partitioning strategies (by date, by source) and file formats (Parquet for analytics, JSON for raw ingestion).
Managed ETL (serverless Spark). Glue Crawlers discover schemas; Glue Data Catalog is a Hive metastore. Know Glue vs EMR tradeoffs.
Managed Spark/Hadoop. For heavy batch processing. Know cost model: transient clusters for batch vs persistent for interactive.
Real-time streaming. Data Streams for ingestion, Firehose for delivery, Data Analytics for processing. Know how it compares to Kafka.
Serverless SQL on S3. No infrastructure. Pay per query. Best for ad-hoc analysis when data is already in S3.
Amazon evaluates technical skills and Leadership Principles with equal weight. Practice SQL at interview difficulty while preparing your LP stories.
Practice SQL Problems