Question 1

Is this collection affiliated with Amazon?

Accepted Answer

**No.** This collection is not affiliated with, endorsed by, or sponsored by Amazon.

The 30 problems are designed to mirror the analytical patterns **publicly reported** in Amazon SQL interviews — sourced from our curated catalog. Production-grade schemas are modeled on the Amazon retail, marketplace, payments, and logistics domains.

*"Amazon-style" describes the format and pattern coverage, nothing more.*

Question 2

Which data roles does this collection prepare me for?

Accepted Answer

**Primary fit:**
- **Data Analyst** — SQL is the gating skill
- **Business Intelligence Engineer (BIE)** — Amazon-specific title where SQL is half the onsite loop
- **Data Scientist (Product / Analytics flavor)**

**Complementary:**
- **Data Engineer** — same SQL bar, but the DE loop adds data-modeling and pipeline-architecture rounds. Pair this with a system-design resource.

**Lower-priority:**
- **Machine Learning Engineer / Applied Scientist** — those loops test minimal SQL. This collection is overkill.

Stage 1 is universal across all roles. Stages 2–5 weight toward DA / BIE / DS in patterns and business framing.

Question 3

How does this collection scale for L6 / Staff / Principal candidates?

Accepted Answer

**SQL difficulty plateaus at L5 / IC4–IC5** across every FAANG company we researched — Amazon, Meta, Google, Apple, Netflix, Stripe.

**What scales at L6+ is depth of probing on the SAME question** — interviewers extend a mid-level problem with optimization, edge cases, and cross-team scope:

> *"How does this hold up across 14 marketplaces / 3B users?"*
> *"What's the query plan? Where would you add an index?"*

Every d3 and d4 explainer in Stages 4 and 5 includes a **"How an L6 interviewer would extend this"** section with 3–5 layered follow-ups grounded in real Amazon onsite reports.

**L6 candidates should do the same 30 problems** — but spend more time on the follow-up section than the base solution.

Question 4

What's the difference between this and the LDS SQL 50?

Accepted Answer

**LDS SQL 50** is a generalist skill progression across 15 industries — adtech, banking, fintech, lodging, logistics, marketplace food and mobility, payments, real estate, retail, SaaS, social graph, streaming media, telecom. It teaches the *patterns*.

**LDS Amazon-Style SQL 30** is a single-loop simulator — every question is either Amazon-tagged in our taxonomy or authored on the Amazon-aligned retail & marketplace schema. The stages mirror the actual *sequence of rounds* a candidate sits in.

There's deliberate QID overlap on a few foundational patterns.

- **Already finished SQL 50?** Jump straight to Stage 3 (First Technical Screen) for Amazon-flavored window-function reps.
- **Starting cold?** Do SQL 50 first, then come here for the loop simulation.

Question 5

Why does the Bar Raiser stage have zero problems?

Accepted Answer

**Because Bar Raisers don't ask live SQL.** Multiple 2025–2026 candidate reports (Prepfully, Hello Interview, Blind threads) confirm Bar Raisers probe **judgment, scope, and Leadership Principles** — not live coding.

The stage stays in the roadmap so candidates see the complete Amazon loop, but it intentionally contains **0 problems** because writing SQL problems for a behavioral round would be dishonest.

Bar Raisers DO probe scenarios where SQL would be the tool:

> *"If you saw a 15% revenue drop, what would you query first, what would you rule out, where would the data lie to you?"*

Those probing scenarios live in **Stage 5 (Onsite Analytical Round)** instead — every d3/d4 explainer there ends with a *"How an L6 interviewer would extend this"* section, which is the same shape Bar Raisers ask.

*For STAR-format Leadership Principles prep, see external behavioral resources — this collection is SQL-only.*

Question 6

How does this transfer to Meta, Google, Stripe, Netflix, and Apple interviews?

Accepted Answer

**Strongly. Each stage maps to a corresponding round at multiple FAANG companies:**

| Stage | Also matches |
|---|---|
| Stage 2 (OA Sprint) | Meta CoderPad screen, Microsoft HackerRank |
| Stage 3 (First Technical Screen) | Meta Technical Coding round, Google SQL phone screen |
| Stage 4 (Onsite SQL Round) | Apple's elimination-grade SQL round, Netflix sessionization deep-dive, Google onsite SQL |
| Stage 5 (Onsite Analytical Round) | Meta Analytical Execution, Stripe analytical-reasoning round |
| Stage 6 (Bar Raiser Scenario) | Behavioral round at every FAANG — empty by design |

The **retail & marketplace schema** (customers, orders, products, payments, shipments) translates cleanly to Walmart, Shopify, Target, eBay, Etsy, Costco, Stripe, and DoorDash data interviews.

*Dedicated company collections for Meta, Google, and Stripe ship separately.*

Question 7

How long does it take to complete all 30?

Accepted Answer

**Roughly 10 hours of focused practice** across 6–9 days at 4–5 problems per day.

- **Stage 5 (Bar Raiser)** takes the most time per problem — 25–30 minutes each for the d3/d4 multi-CTE pipelines
- **+10 more minutes per problem** if you do the L6 probing follow-ups
- **L6+ candidates** should plan **12–14 hours total**

The collection is **free up through Stage 3** (50% of the problems), so you can validate fit before committing.

Question 8

Can my company or bootcamp use these problems in our prep program?

Accepted Answer

**Yes** — the 30-problem structure is designed for reuse.

Recruiting teams running internal SQL bootcamps, university partners doing on-campus prep, and Big4 consulting hiring desks have asked us about co-branded access.

- **Framework is portable:** Baseline Fluency Check → OA Sprint → First Technical Screen → Onsite SQL Round → Bar Raiser Scenario
- **Question pool is licensable**
- **Canonical attribution link:** `letsdatascience.com/problems/collections/lds-amazon-style-sql-30`

For volume licensing or co-branding, reach out via the contact form.

Question 9

How does this compare to DataLemur, StrataScratch, or LeetCode for Amazon interview prep?

Accepted Answer

Tagged-question catalogs help you drill Amazon-style problems. But Amazon's loop isn't a problem list — it's a **5-round process** with the Bar Raiser at the end.

**What LDS Amazon-Style SQL 30 gives you that they don't:**

- **A 6-stage Amazon onsite simulator** — OA Sprint → Technical Phone Screen → SQL & Data Modeling → Onsite Analytics → Business Scorecards → Bar Raiser (intentionally empty; behavioral)
- **A retail & marketplace schema modeled on Amazon's data surface** — orders, fulfillment, shipping, customer reviews, third-party sellers
- **Leadership Principles tagging on every d3/d4 explainer** — see which LP the question screens for
- **L6+ scope-design probing follow-ups** — "how does this hold up across 14 marketplaces / 3B users?"
- **Bar Raiser scenarios** in the Onsite Analytical stage — the same shape Bar Raisers actually ask
- **Both SQLite and PostgreSQL** on every problem
- **Half the collection is free** (15 of 30 problems) — concentrated in OA Sprint and Phone Screen difficulty

*Catalogs give you Amazon-tagged questions. This collection gives you the Amazon interview itself.*

LDS Amazon-Style SQL 30

About this collection

Your Learning Path

Why These 30?

A 6-Stage Amazon Loop Simulator

A Retail & Marketplace Schema Modeled on Amazon

Leadership Principles + L6+ Probing Built In

Skill Coverage

FAQ

Ready for your Amazon interview?

LDS Amazon-Style SQL 30

About this collection

Your Learning Path