amazon-data-scientist-interview-sql-questions-2026

Amazon data scientist interviews focus on multi-step aggregation problems involving window functions and self-joins rather than simple filtering. You will rarely see a question asking you to select columns from a single table.

TL;DR

The standard Amazon SQL question involves at least three tables and requires you to handle null values, deduplicate records, or calculate running totals across specific time windows. A recurring question from the Prime Video analytics loop in 2023 asked candidates to identify users who watched more than 50% of a series but stopped before the finale, then calculate the churn probability based on their viewing gap.

This requires a self-join to compare episode completion rates against total episode counts. Another common prompt from the AWS Marketplace team involves calculating month-over-month growth while excluding accounts that were flagged for fraud in the previous cycle. This forces you to write a subquery or CTE that filters state before performing the time-series calculation. The problem is not your ability to type GROUP BY. The problem is your ability to structure a query that respects business logic constraints before aggregation.

title: "Amazon Data Scientist Interview Sql Questions"

slug: "amazon-data-scientist-interview-sql-questions-2026"

segment: "jobs"

lang: "en"

keyword: "Amazon data scientist interview sql questions"

company: "Amazon"

school: ""

layer: L3-question

type_id: ""

date: "2026-06-17"

source: "factory-v2"

The candidates who memorize the most syntax often fail the Amazon data scientist loop. In a Q4 2023 hiring committee for the Alexa Shopping team, a candidate recited perfect window function syntax but could not explain why a LEFT JOIN would explode row counts on a many-to-many relationship. The committee voted no-hire. They did not reject the candidate for lacking SQL knowledge.

They rejected the candidate for lacking data intuition. Amazon does not hire coders. Amazon hires owners who understand how data moves through distributed systems. Your ability to write a query is the baseline expectation, not the differentiator. The differentiator is your judgment on when not to run a query at all.

What specific SQL questions does Amazon ask data scientist candidates?

In a debrief for a Senior Data Scientist role on the Kindles team, the hiring manager noted that the candidate spent twelve minutes optimizing a query for speed before confirming the definition of an "active reader." The candidate assumed an active reader was anyone who opened a book. The business definition required a minimum of ten minutes of engagement. The query was technically flawless but logically useless. Amazon interviewers look for this specific failure mode.

They want to see you ask clarifying questions about edge cases before writing a single line of code. If you jump straight to writing a complex Common Table Expression without asking how to handle cancelled orders or returned items, you signal that you prioritize syntax over business impact.

The first counter-intuitive truth is that a simpler query with correct business logic beats a complex query with perfect syntax every time. Amazon values the "Working Backwards" principle even in coding rounds. You must define the customer outcome before defining the join condition.

How do Amazon interviewers evaluate SQL solutions during the live coding round?

Amazon interviewers evaluate SQL solutions based on readability, modularity, and the explicit handling of edge cases rather than execution speed or clever one-liners. During a live coding session on Codeworx or a shared doc, the interviewer is watching your thought process, not just your final output.

They are looking for evidence of the "Bias for Action" leadership principle, which in a SQL context means making reasonable assumptions when data is missing and documenting them clearly. In a 2024 loop for the Advertising Tech team, a candidate lost the round because they used a nested subquery that was impossible to debug when the interviewer introduced a twist regarding duplicate user IDs. The candidate had written a monolithic block of code.

A second candidate solved the same problem using three distinct CTEs, labeling each one with a comment explaining its purpose. The second candidate got the offer. The difference was not the result. The difference was the maintainability of the solution. Amazon operates at a scale where unreadable SQL causes outages.

The evaluation rubric specifically penalizes hard-coded values and implicit type conversions. If you write WHERE date > '2023-01-01' without casting the string to a date object, an Amazon interviewer will flag this as a risk for production environments. In a debrief for the Supply Chain Optimization Technologies (SCOT) team, a candidate was rejected because they assumed all timestamps were in UTC. The dataset included mixed time zones from global fulfillment centers. The candidate failed to normalize the data before aggregation.

This is a classic Amazon trap. They intentionally provide messy data to see if you clean it. The second counter-intuitive truth is that spending the first five minutes of a 45-minute round discussing data hygiene and schema design is a stronger signal of seniority than solving the problem in thirty minutes.

Junior candidates rush to code. Senior candidates rush to understand the data model. If you do not ask about primary keys, foreign key relationships, or data freshness, you are signaling that you are not ready for the scale of Amazon's data lake.

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Why do candidates with strong LeetCode SQL skills fail Amazon onsite loops?

Candidates with strong LeetCode SQL skills fail Amazon onsite loops because they optimize for algorithmic complexity rather than data volume and cost implications. LeetCode problems assume infinite compute and perfect data. Amazon problems assume petabyte-scale tables where a full table scan costs real money and slows down downstream dashboards. In a Q1 2024 debrief for the Prime Membership team, a candidate proposed a solution using a CROSS JOIN to generate a date grid. Technically, this worked for the small sample size provided.

However, the interviewer pointed out that on the actual production table with two billion rows, this query would crash the cluster. The candidate argued that the syntax was correct. The committee voted no-hire. The candidate failed to demonstrate "Frugality," a core leadership principle. They were willing to waste compute resources to solve a problem quickly. Amazon data scientists are expected to be stewards of company resources.

The failure often stems from a lack of understanding regarding distributed SQL engines like Presto or Redshift. On LeetCode, you write standard ANSI SQL. At Amazon, you must understand how data is partitioned and sorted. If you write a query that forces a massive shuffle operation across nodes because you ignored the distribution key, you will fail the bar. In an interview for the Alexa AI team, the interviewer asked a candidate to optimize a query that was timing out. The candidate added more indexes.

The correct answer was to rewrite the query to push down predicates before the join, reducing the data shuffled across the network. The candidate did not know this distinction. They treated the database as a black box.

Amazon needs engineers who understand the engine. The third counter-intuitive truth is that knowing when not to use SQL is sometimes the right answer. If a problem requires complex iterative logic, a Python script within a Spark job might be the frugal and scalable choice. Insisting on SQL for everything signals a lack of architectural breadth.

What is the connection between Leadership Principles and SQL query design?

The connection between Leadership Principles and SQL query design is direct and often the deciding factor in hiring decisions. Every line of code you write in an Amazon interview is evidence for a specific leadership principle. "Customer Obsession" translates to ensuring your query handles edge cases that affect the customer experience, such as null values in pricing columns or negative quantities in order logs. "Bias for Action" means proposing a reasonable default when data is ambiguous rather than stalling.

"Dive Deep" requires you to inspect the data distribution before aggregating, checking for skew that might invalidate your average calculations. In a hiring committee meeting for the Fresh Grocery team, a candidate was championed because their SQL solution included a specific check for outliers in delivery times, citing a potential customer pain point. Another candidate was rejected because their query silently dropped records with missing geo-coordinates, which would have hidden a systemic tracking failure. The code was functionally similar. The judgment was vastly different.

You must explicitly narrate your thought process through the lens of these principles. Do not just say "I am using a LEFT JOIN." Say "I am using a LEFT JOIN to ensure we do not lose customers who have signed up but not yet placed an order, aligning with Customer Obsession." This framing changes the perception of your technical skill from mechanical to strategic.

In a 2023 loop for the Seller Services team, a candidate explicitly mentioned "Frugality" when choosing to filter data in the WHERE clause before joining, explaining that this reduces scan bytes and saves costs. The interviewer noted this in the feedback form. It tipped the vote from lean no-hire to lean hire.

Amazon interviewers are trained to map behaviors to principles. If you do not make the mapping obvious, they will not assume it exists. They will assume you are just writing code. The problem is not your SQL proficiency. The problem is your failure to connect your technical choices to the company's cultural operating system.

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Preparation Checklist

Simulate a messy data scenario by taking a clean dataset and intentionally introducing nulls, duplicates, and mixed time zones, then write a query that handles each anomaly explicitly while commenting on the business impact of each decision.
Practice explaining your query optimization choices in terms of "Frugality" and "Scale," specifically articulating how your predicate pushdown or join strategy reduces compute costs on a petabyte-scale dataset.
Review the schema designs of large-scale public datasets (like those on AWS Open Data) to understand partitioning strategies and distribution keys, then rewrite a naive query to align with those physical storage realities.
Work through a structured preparation system (the PM Interview Playbook covers data intuition and metric definition frameworks with real debrief examples) to ensure you define business logic before writing syntax.
Record yourself solving a medium-difficulty SQL problem and critique whether you asked at least three clarifying questions about data quality and edge cases before typing your first keyword.
Memorize the exact syntax for window functions (ROW_NUMBER, RANK, LAG, LEAD) in Redshift and Presto, noting any dialect-specific differences that could cause errors in a production environment.
Prepare three specific stories where you identified a data quality issue that would have skewed a business metric, framing the narrative around the "Dive Deep" leadership principle.

Mistakes to Avoid

BAD: Immediately writing a complex nested query without asking about the primary key or how duplicates are handled.

GOOD: Asking "Are user IDs unique in this table, or do we need to deduplicate based on timestamp?" before writing any code, then implementing a deduplication step in a CTE.

BAD: Using a subquery to filter data after a join has already occurred, causing unnecessary data shuffling.

GOOD: Pushing the filter condition into the WHERE clause of the base table selection or the ON clause of the join to reduce the dataset size before the shuffle operation.

BAD: Ignoring null values in aggregation functions, leading to skewed averages or counts.

GOOD: Explicitly handling nulls with COALESCE or filtering them out with a clear justification, stating "I am excluding nulls here because a missing price indicates a data pipeline error, not a free product."

FAQ

Do I need to know Python for the Amazon data scientist SQL round?

No, the SQL round is strictly for SQL. However, if the problem requires logic that SQL cannot handle efficiently, mentioning that you would switch to Python or Spark for that specific step demonstrates architectural judgment. Do not write Python code unless asked.

What is the salary range for Amazon data scientists?

Base salaries for L5 Data Scientists typically range from $145,000 to $165,000, with sign-on bonuses varying between $40,000 and $75,000 in the first two years. Total compensation including stock often reaches $220,000 to $260,000, depending on the specific organization and location.

How many rounds of SQL interviews are there at Amazon?

You will typically face one dedicated SQL coding round during the onsite loop, lasting 45 to 60 minutes. However, SQL concepts may appear in the data modeling round or the behavioral round if you discuss technical projects in your "Leadership Principles" stories.

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