The Amazon Data Engineer SQL Interview — What's Different

Amazon evaluates SQL differently than Meta or Google. Meta tests product judgment. Google tests ambiguity. Amazon tests whether your code would survive a code review at a company where data pipelines run a $600B business.

That difference shows up everywhere in the loop. The questions are more execution-oriented. The bar for "production-ready SQL" is higher. And the leadership principles aren't just on the behavioral round — they bleed into how your SQL gets graded.

This is what to expect and how to prepare.

The Amazon Data Engineer Loop

The data engineer loop at Amazon typically runs four to five rounds. Two of them have heavy SQL: the technical screen and the on-site coding round. A third — the design round — touches SQL through schema decisions and query patterns.

What gets tested across these rounds:

1. SQL correctness under realistic schemas (not toy tables — multi-table joins with FKs, NULLs, and edge cases)
2. Query performance awareness (would this query actually run on a billion-row table?)
3. Production-readiness (defensive guards, idempotency, readability)
4. Behavioral framing through the lens of Amazon's Leadership Principles

If you've only practiced LeetCode SQL, you're prepared for one-third of the loop. The other two-thirds are about how your code holds up under scale and review.

What "Production-Ready SQL" Means At Amazon

This phrase shows up in Amazon engineering culture and it has a specific meaning. Production-ready SQL is code that:

• Handles NULLs explicitly (no silent failures on WHERE col = NULL)
• Guards against division by zero
• Uses explicit JOIN syntax, not comma joins
• Aliases tables consistently
• Names CTEs by what they contain, not by t1, t2, cte_a
• Returns deterministic results (no implicit ordering)
• Won't crash if a referenced row is missing

Most candidates write queries that are correct in the demo but wouldn't pass a code review. That gap is where Amazon loops most often go sideways for otherwise-strong candidates.

Example of the gap:

A typical LeetCode answer:

SELECT user_id, COUNT(*) AS orders
FROM orders
WHERE order_date > '2024-01-01'
GROUP BY user_id
HAVING COUNT(*) > 5;

The same query, production-ready:

SELECT
  o.user_id,
  COUNT(*) AS order_count
FROM orders o
WHERE o.order_date >= DATE '2024-01-01'
  AND o.order_status NOT IN ('cancelled', 'refunded')
GROUP BY o.user_id
HAVING COUNT(*) > 5
ORDER BY o.user_id;

The differences look minor — table alias, explicit DATE literal, >= instead of >, exclusion of cancelled orders, deterministic ordering. Each one is a small senior-credit signal. The whole list is what production-ready means.

The Four Question Types You'll See

1. The volume question

"Given a table of 10 billion order rows, compute revenue by product category for the last 7 days."

The setup is the trap. The answer isn't the SQL — the answer is what you say about running the SQL.

The expected senior moves:

• Filter early. Apply the date predicate as the first thing the query does, before any join.
• Avoid SELECT * on a fact table.
• Mention that this query benefits from a date-partitioned table — and if there isn't one, that's worth a follow-up.
• Aggregate before joining if you can.

WITH recent_orders AS (
  SELECT
    o.product_id,
    o.order_total
  FROM orders o
  WHERE o.order_date >= CURRENT_DATE - INTERVAL '7 days'
    AND o.order_status = 'completed'
)
SELECT
  p.category,
  SUM(r.order_total) AS revenue
FROM recent_orders r
JOIN products p ON p.product_id = r.product_id
GROUP BY p.category
ORDER BY revenue DESC;

Note the structure: filter and slim the fact table first, then join the dimension. On a billion-row table this is the difference between a query that returns in seconds and one that times out.

2. The deduplication question

Amazon loves these. They give you a table that, in real life, has duplicates from upstream pipeline issues, and ask you to deduplicate "correctly."

"The customer_orders table has duplicate rows where the same order_id appears multiple times. Return one row per order_id, keeping the most recent version (highest updated_at)."

The wrong answer is SELECT DISTINCT *. The right answer uses ROW_NUMBER():

WITH ranked AS (
  SELECT
    co.*,
    ROW_NUMBER() OVER (
      PARTITION BY co.order_id
      ORDER BY co.updated_at DESC
    ) AS rn
  FROM customer_orders co
)
SELECT *
FROM ranked
WHERE rn = 1;

This pattern shows up in every Amazon SQL screen I've heard about. Memorize it. The variant they sometimes ask is to deduplicate on a composite key (order_id, line_item_id), which is the same pattern with a longer PARTITION BY clause.

3. The incremental load / idempotency question

These questions don't look like SQL questions at first — they look like design questions.

"You have a daily job that loads new orders into a summary table. How do you make sure re-running the job for the same day doesn't double-count?"

The answer is MERGE (or its equivalent INSERT ... ON CONFLICT):

MERGE INTO daily_revenue_summary tgt
USING (
  SELECT
    DATE(order_date) AS day,
    SUM(order_total) AS revenue
  FROM orders
  WHERE order_date >= CURRENT_DATE - INTERVAL '1 day'
    AND order_date <  CURRENT_DATE
    AND order_status = 'completed'
  GROUP BY DATE(order_date)
) src
ON tgt.day = src.day
WHEN MATCHED THEN UPDATE SET revenue = src.revenue
WHEN NOT MATCHED THEN INSERT (day, revenue) VALUES (src.day, src.revenue);

What the interviewer is checking: do you know the difference between an append-only load (cheap but breaks on re-runs) and an upsert (slightly more expensive but idempotent). Production code at Amazon is almost always idempotent — that's the senior signal.

4. The aggregation with windowing twist

Same pattern as Meta and Google but with one extra dimension: Amazon tends to ask the window question in a way that requires you to think about partitioning explicitly.

"For each warehouse, find the day with the highest order volume and the day with the lowest, within the last 90 days."

WITH daily AS (
  SELECT
    warehouse_id,
    DATE(order_date) AS day,
    COUNT(*) AS order_count
  FROM orders
  WHERE order_date >= CURRENT_DATE - INTERVAL '90 days'
  GROUP BY warehouse_id, DATE(order_date)
),
ranked AS (
  SELECT
    warehouse_id,
    day,
    order_count,
    ROW_NUMBER() OVER (PARTITION BY warehouse_id ORDER BY order_count DESC) AS rank_high,
    ROW_NUMBER() OVER (PARTITION BY warehouse_id ORDER BY order_count ASC)  AS rank_low
  FROM daily
)
SELECT
  warehouse_id,
  MAX(CASE WHEN rank_high = 1 THEN day         END) AS highest_day,
  MAX(CASE WHEN rank_high = 1 THEN order_count END) AS highest_count,
  MAX(CASE WHEN rank_low  = 1 THEN day         END) AS lowest_day,
  MAX(CASE WHEN rank_low  = 1 THEN order_count END) AS lowest_count
FROM ranked
GROUP BY warehouse_id;

Two window functions in a single CTE is a clean pattern that interviewers like. The pivot at the end (CASE WHEN inside MAX) is the move that turns a tall result into a wide one — useful any time you're collapsing per-group ranks into a single row.

The Leadership Principle Overlay

Amazon's interviewers are trained to map your behavior in the SQL round back to the Leadership Principles. The two that show up most:

Dive Deep. This is why Amazon weights the volume / performance questions so heavily. When asked about a big table, candidates who casually say "I'd filter early, push the join after the predicate, and check whether there's a partition I can leverage" signal Dive Deep instinctively.

Insist On The Highest Standards. The production-readiness checklist above is essentially this principle made concrete. Every defensive guard you write — the NULLIF, the explicit join, the deterministic order — is a small data point that says: I won't ship code that breaks the moment it leaves my laptop.

Don't recite the LPs in the SQL round. Just behave consistently with them, and the interviewer's score sheet will fill itself in.

Three Pre-Loop Habits Worth Building

Write every query with a partner-mode mindset. Before you submit a query mentally, ask: Would I be embarrassed if a senior engineer on my future team saw this? If yes, fix it.

Practice on schemas with realistic mess. Real Amazon tables have NULLs, partial duplicates, soft-deleted rows (is_deleted = TRUE), and out-of-order events. Practice on data that resembles that, not on hand-cleaned toy tables.

Time-box yourself to 25 minutes per question. That's the interview budget. If you can't get a working answer in 25, you don't have the working answer. Speed under pressure is part of the skill.

The Quiet Bar Most Candidates Miss

Across every Amazon loop story I've heard, one signal separates "competent" from "offer": the candidate caught a real issue with the question itself. "The schema you described has a foreign key from orders to products, but products has a soft-delete flag — should we exclude rows where the product is deleted? I noticed you didn't mention it."

That's senior engineer behavior. It's also extremely hard to fake. You build it by writing thousands of queries against data that actually has those quirks, and noticing them every time. Which brings us to the final move: pick a practice platform that gives you that surface, not one that gives you LeetCode-style clean inputs.