FAANG data engineering interviews run 4-6 rounds, each testing something different. SQL with optimization follow-ups. Python with production-quality expectations. System design at petabyte scale. Behavioral rounds that actually matter. DataDriven's 1,000+ questions are authored by engineers from Meta, Google, Amazon, Netflix, and Uber who conduct these interviews.
Company-specific prep tracks. Real code execution. AI grading calibrated against FAANG interviewers.
FAANG-Level Questions
Company Prep Tracks
Average Rounds Per Loop
Users Got FAANG Offers
A mid-size company's data engineering interview loop is usually 3 rounds: a phone screen with SQL, a coding round with Python, and a "design something" round that's fairly open-ended. If you can write correct SQL, produce clean Python, and draw a reasonable architecture diagram, you'll get an offer.
FAANG is different. The loop runs 4-6 rounds, each with a specific evaluation rubric. The SQL round doesn't just test correctness; it tests optimization, edge case handling, and your ability to explain trade-offs in your approach. The coding round expects production-quality code with error handling, type hints, and testable structure. System design questions specify exact constraints: "1B events/day, 99.9% SLA, 5-second freshness." Your design must meet those numbers.
The behavioral round is the biggest difference. At non-FAANG companies, behavioral is a 15-minute warm-up. At FAANG, it's a full 45-minute round, and a weak performance can veto an otherwise strong candidate. At Amazon, two of the five onsite rounds are behavioral (LP-focused). At Netflix, the culture round is weighted equally to technical rounds. Even at Google, the "Googleyness and Leadership" round can tip a borderline candidate from "hire" to "no hire."
The calibration bar is higher too. FAANG interviewers are trained to evaluate candidates against a level-specific rubric. An L5 candidate at Google needs to demonstrate independent technical leadership and the ability to make design decisions without guidance. An L4 needs to show strong execution on well-defined problems. The same answer that passes at L4 fails at L5 because the interviewer expects more depth, more alternatives considered, and more awareness of failure modes.
Each FAANG company tests data engineers differently. Here's what to expect and how DataDriven prepares you for each one.
Phone screen (SQL) + 5 onsite loops (2 coding, 1 system design, 2 behavioral)
Amazon's data engineering interviews are unique because Leadership Principles (LPs) are woven into every round, including technical ones. Your system design answer for a real-time inventory pipeline isn't just evaluated on technical merit. The interviewer is also listening for 'Bias for Action' (why you chose a pragmatic solution over a perfect one) and 'Dive Deep' (whether you can explain the details of your chosen storage layer). Expect at least one LP-focused follow-up in every technical round.
COMMON QUESTIONS
Design a clickstream analytics pipeline for Amazon.com. Write SQL to find products with declining sales over 3 consecutive months. Build a Python ETL for processing seller reviews with sentiment scoring. Explain how you'd handle late-arriving data in a Kinesis-based pipeline.
DATADRIVEN COVERAGE
DataDriven has 120+ questions tagged to Amazon interview patterns, including LP-integrated technical prompts where the AI evaluates both your technical answer and how well you articulate your decision-making process.
Phone screen (coding) + 4-5 onsite loops (2 coding, 1-2 system design, 1 Googleyness)
Google interviews for data engineers skew heavily toward scale. Every question assumes petabyte-scale data. If your system design doesn't address sharding, replication, and failure recovery at scale, you won't pass. Google also tests SQL more rigorously than other FAANG companies; expect complex window functions, recursive CTEs, and optimization questions. The 'Googleyness' round evaluates collaboration and communication, not just culture fit.
COMMON QUESTIONS
Design a data pipeline for Google Maps traffic data (1B events/day). Write SQL to detect anomalies in ad click data using statistical methods. Implement a Python solution for deduplicating events across multiple data sources. Design a feature store for ML models serving 10M predictions/second.
DATADRIVEN COVERAGE
DataDriven's Google-tagged questions emphasize scale constraints. Each problem specifies data volume, and the AI grader evaluates whether your solution handles the stated scale, not just correctness on small data.
Phone screen (SQL) + 4 onsite loops (1 SQL, 1 coding, 1 system design, 1 behavioral)
Meta's data engineering interviews are the most data-modeling-intensive of any FAANG company. The SQL round isn't just about writing correct queries. It's about designing schemas that support the queries the business will need. Expect questions about slowly changing dimensions, fact table granularity, and how to model social graph data. Meta also places heavy weight on the system design round, where you'll design pipelines for products like News Feed, Marketplace, or Instagram Reels.
COMMON QUESTIONS
Design a data model for Facebook Marketplace (listings, transactions, reviews, seller ratings). Write SQL to compute 28-day rolling active users with daily granularity. Build a pipeline that processes Instagram story view events in near real-time. Design a data warehouse schema for A/B test results across multiple product surfaces.
DATADRIVEN COVERAGE
DataDriven has 85+ Meta-pattern questions with particular depth in data modeling. The AI grader evaluates schema designs and flags issues like missing indexes, grain mismatches, and overlooked access patterns.
Phone screen (coding) + 4 onsite loops (1 coding, 2 system design, 1 culture)
Netflix interviews stand out for two reasons: streaming architecture and the culture round. Netflix processes 400B+ events daily for real-time personalization, and their data engineering interviews reflect this. Expect system design questions about real-time data pipelines, event sourcing, and stream processing. The culture round at Netflix is genuinely different from behavioral rounds at other companies. They test for 'freedom and responsibility' by asking about times you made decisions without asking permission and how those decisions turned out.
COMMON QUESTIONS
Design a real-time recommendation pipeline that updates as users watch content. Write PySpark code to process viewing session data and compute engagement metrics. Design a data quality monitoring system for a streaming pipeline processing 5M events/second. Explain how you'd migrate a batch ETL to near real-time without downtime.
DATADRIVEN COVERAGE
DataDriven's Netflix-tagged questions focus on streaming architecture and real-time processing. The pipeline architecture domain covers event-driven design patterns that appear in Netflix interviews.
Phone screen (SQL + coding) + 4-5 onsite loops (2 coding, 1-2 system design, 1 behavioral)
Uber's data engineering interviews are heavy on geospatial data, real-time processing, and infrastructure at scale. The coding rounds often involve PySpark and expect production-quality code, not whiteboard pseudocode. Uber uses a shared coding environment where you write and run PySpark during the interview. System design questions frequently involve location data, pricing algorithms, and marketplace dynamics. Uber also asks about data governance and compliance (GDPR, data retention) more than other FAANG companies.
COMMON QUESTIONS
Design a surge pricing data pipeline that processes ride requests in real-time. Write PySpark code to compute driver supply and demand metrics by geospatial grid. Design a data lake architecture for storing and querying trip data across 70+ countries. Build a pipeline for computing ETA predictions using historical trip data.
DATADRIVEN COVERAGE
DataDriven covers Uber's emphasis on PySpark with real execution environments. Questions tagged to Uber patterns include geospatial aggregations and real-time pipeline design scenarios.
Most FAANG data engineering loops follow a similar structure, with company-specific variations. Here's what a typical loop looks like.
Phone Screen (45 min): Usually SQL, sometimes Python. You share your screen and write code in a collaborative editor. The interviewer watches you code in real-time and asks follow-up questions. "Your query returns the right answer. Now the table has 500M rows. What changes?" This round filters roughly 60% of candidates.
Coding Round 1 (45 min): SQL with optimization. You write a query, then the interviewer adds constraints. "Good. Now add a window function for running totals. Now handle ties. Now optimize for a table with 1B rows." Each follow-up reveals whether your initial solution was designed to extend or just hacked to pass the basic case.
Coding Round 2 (45 min): Python or PySpark. Expect a data transformation problem: parse log files, compute metrics from event data, or build a data validation framework. The interviewer evaluates your code structure, error handling, and whether you write testable functions or monolithic scripts.
System Design (60 min): The highest-weighted round for L5+. "Design a real-time analytics pipeline for [our product]." You drive the conversation: clarify requirements, propose a high-level architecture, dive into storage and processing choices, discuss failure modes and monitoring. The interviewer evaluates your ability to make and defend design decisions under ambiguity.
Behavioral (45 min): Varies by company. Amazon: deep LP stories. Google: collaboration and leadership examples. Meta: how you handle conflict and ambiguity. Netflix: freedom and responsibility. The format is STAR (Situation, Task, Action, Result), but interviewers probe for specifics. Vague stories fail.
The most common failure mode isn't lack of knowledge. It's lack of interview-specific practice. Engineers who build production pipelines daily still fail because the interview format tests different skills than the job does.
Failure #1: Correct but slow. You write a correct SQL query in 25 minutes, but the round has 3 follow-ups. At FAANG pace, the initial query should take 8-10 minutes, leaving time for optimization, extensions, and discussion. DataDriven's timed mock interviews train you for this pace. You solve problems repeatedly until speed is automatic.
Failure #2: Can't explain decisions. You choose Kafka for event ingestion but can't explain why Kafka over Kinesis, Pub/Sub, or a simple file-based approach. FAANG interviewers probe every decision. "Why this storage layer?" "What are the alternatives?" "What breaks first at 10x scale?" DataDriven's discuss mode simulates these follow-ups.
Failure #3: Weak behavioral stories. You have great technical stories but tell them poorly. Vague: "I improved the pipeline." Strong: "I reduced pipeline runtime from 4 hours to 22 minutes by replacing a correlated subquery with a pre-aggregated JOIN, which unblocked the morning analytics report for 200 analysts." Specificity is what separates pass from fail.
Failure #4: Underestimating the system design bar. At L5+, system design carries 30-40% of the overall evaluation. Candidates who spend 80% of their prep on coding and 20% on design get caught. The design round at FAANG requires you to lead a 60-minute conversation, make real-time decisions, and handle curveballs. You can't wing it.
WEEKS 1-2
Solve 40-50 SQL problems across window functions, CTEs, JOINs, and aggregations. Focus on correctness and speed. By end of week 2, you should solve medium-difficulty SQL in under 10 minutes. DataDriven's AI grading catches the edge case bugs you won't notice on your own.
WEEKS 3-4
30-40 Python problems focused on data transformation, pandas operations, and production-quality code. Add 15-20 PySpark problems if targeting Netflix, Uber, or any role that processes more than 1TB. Write code that handles errors, not just the happy path.
WEEKS 5-6
Practice 8-10 system design problems end to end. For each one, spend 45 minutes designing, then review your design against DataDriven's reference architecture. Focus on data modeling for Meta, pipeline design for Netflix, and scale for Google. Every design should address failure modes, not just the happy path.
WEEKS 7-8
Filter DataDriven questions by your target company. Practice full mock interviews (all rounds) with time constraints. Prepare 8-10 behavioral stories using the STAR framework. For Amazon, map each story to 2-3 Leadership Principles. Do at least one full mock interview with a human for verbal communication practice.
Three main differences. First, more rounds: FAANG companies run 4-6 onsite loops vs. 2-3 at smaller companies. Second, higher system design bar: you're expected to design for petabyte scale, not gigabyte scale. Third, behavioral rounds matter more. At non-FAANG companies, behavioral is often a checkbox. At FAANG, a weak behavioral can sink an otherwise strong technical performance. The coding bar is roughly the same, but FAANG expects you to discuss trade-offs and alternatives, not just produce correct output.
Google, based on pass rates. Google's interview process has the lowest offer rate among FAANG data engineering roles (roughly 15-20% of onsites result in offers). The SQL bar is the highest, the system design questions assume the most scale, and the Googleyness round is subjective. Meta is second-hardest because of the data modeling depth. Amazon is the most approachable because the LP framework gives you a clear rubric to prepare against.
For about 60% of the prep, yes. SQL fundamentals, Python coding, and basic system design principles apply everywhere. The other 40% should be company-specific. Amazon requires LP prep. Google requires scale-focused design. Meta requires deep data modeling knowledge. Netflix requires streaming architecture. Uber requires PySpark proficiency. DataDriven lets you filter questions by company to focus on the patterns each company tests.
For candidates with 3+ years of experience: 6-8 weeks of focused prep, 1-2 hours daily. That breaks down to roughly 2 weeks on SQL, 2 weeks on Python and PySpark, 2 weeks on system design, and 1-2 weeks on behavioral and company-specific patterns. Candidates with less experience or switching from a different role should plan for 10-12 weeks. DataDriven users average 45 days from signup to interview with a 73% offer rate at FAANG companies.
Yes. Every question in our catalog is authored or reviewed by an engineer who has conducted data engineering interviews at Meta, Google, Amazon, Netflix, or Uber. We don't scrape Glassdoor or repackage LeetCode problems. Each question specifies the company pattern it maps to, the interview round it targets (phone screen, coding, system design), and the level it's calibrated for (L3-L7).
Questions from the engineers who conduct these interviews. Real code execution. AI grading calibrated against FAANG rubrics. 73% of DataDriven users who complete a full prep cycle receive an offer.