FAANG Cram

40 hard problems across SQL, Python, data modeling, and pipeline architecture. No warmups.

This study plan is 40 curated data engineering interview problems grouped for structured practice. Each challenge runs real code against live databases or executes Python in a sandboxed environment. You get instant grading, company tier filtering, seniority calibration, and spaced repetition that keeps weak concepts in rotation until they stick.

How to Use This Plan

Start with the easier items to build pattern recognition for the domain.
Move to medium and hard challenges once you can solve easy ones without hints.
After solving a challenge, read the AI discussion phase prompts to pressure-test your solution.
Track your readiness score on your profile and retry items where you struggled.
When you feel ready, launch a full mock interview at /interview for end-to-end simulation.

SQL Challenges (10)

Longest Visit Streaks - hard - Day after day after day. Who kept coming back?
Previous Day Top Service - hard - Yesterday's top spender.
Quarter-over-Quarter Latency Trend - hard - Latency trending up or down? The quarters have the answer.
Campaign Conversion Window - hard - A narrow window between impression and action.
Flatten Org Chart Hierarchy - hard - The tree runs deep. Walk every branch to the root.
First-Day Session Retention - hard - Day one retention. The first test.
The Org Chart in Numbers - hard - Headcount by department, sliced by quarter. Every seat accounted for.
Experiment Variant Ratios - hard - Control versus treatment. The participation split.
Adopters Before Migration - hard - They used the old feature. Did they ever touch the new one?
Model Accuracy Drift - hard - Accuracy used to be higher.

Python Challenges (10)

The DAG Executor - hard - Wire up a mini pipeline and watch it run.
The Throttle Wall - hard - Stop the abusers. Let the rest through.
Stream-Process a Large CSV - hard - Too big to load. Read what you can.
The Change Data Capture - hard - Inserts, updates, deletes : all present.
The Stream Joiner - hard - Events don't wait for each other. This does.
The Hierarchy Builder - hard - Parent-child pairs, flat. Build the family tree.
The Anomaly Detector - hard - Spot the outliers before they page someone.
The Meeting Room Allocator - hard - Meetings overlap on the calendar. Rooms are limited.
The Schema Migrator - hard - Old schema in, new schema out.
The Output Peak - hard - One stretch outpaced all the others.

Data Modeling Challenges (10)

E-Commerce Supply Chain Tracking - hard - A package splits, reroutes, and (maybe) arrives.
Financial Trading Warehouse - hard - Every trade, every tick, every fraction of a share. The regulators want receipts.
Insurance Claims Lifecycle - hard - A claim gets filed. Then it gets complicated. Then it gets reassigned. Then it loops back.
Property Booking Platform - hard - Five-star listing. Three-star reality.
The Customer Who Changed - hard - She moved. She upgraded. She became someone new. The record has to keep up.
Telecom Network Connectivity Warehouse - hard - One device goes down. The ripple keeps going.
Online Marketplace - Seller Payouts - hard - The buyer paid one number. The seller got a different one.
Cloud File Storage Metadata Schema - hard - A file is also a folder. A folder is also a file.
Three-Sided Marketplace Delivery Schema - hard - One order. Two deliveries. Revenue counted twice. Where is the bug in your schema?
Metric Definition Reverse Engineering - hard - Five numbers on a dashboard. Your job: figure out where they come from.

Pipeline Architecture Challenges (10)

Score It Before It Clears - hard - The fraudsters move fast. Your pipeline has to move faster.
Replicate It Without Breaking It - hard - The source changed. The lake needs to know immediately.
A Stream All Day and a File at Midnight - hard - Real-time and batch. Same pipeline. No compromises.
Event System for Multiple Consumers - hard - One event, many hungry consumers.
One Earthquake, Ten Thousand Tweets - hard - The firehose is on. Separate signal from noise.
Prove the Number Is Right - hard - Bad data in fintech is not just messy. It is expensive.
Three Regions, One Finance Team - hard - Payments from everywhere. One consistent report.
The Models Going Stale - hard - The model is only as good as what you feed it.
Live Viewers, Live Billing - hard - The stream is live. The data cannot wait.
Two Years of Every Click - hard - Every click, every aisle, every day for two years.