What I Look for When I Interview System Design Candidates

I've sat on both sides of the system design interview table. Eight years at Google running distributed systems, and now I evaluate candidates as CTO of a fintech startup. Here's what I've noticed: most engineers prepare for system design interviews the same way they prepare for coding interviews. They memorize solutions. They collect design patterns like Pokémon cards. They watch YouTube videos at 2x speed.

Then they walk into their Google interview and freeze when asked something slightly different from what they practiced.

The problem isn't effort. It's mindset. As an engineer, you optimize for the right answer. As an interviewer, I'm evaluating something entirely different: can you navigate ambiguity, communicate trade-offs, and demonstrate senior-level thinking under pressure? My system design interview prep guide for FAANG isn't about memorizing more architectures. It's about reverse-engineering what actually separates passes from failures.

I analyzed my notes from 50+ system design interviews I've conducted and participated in. Patterns jumped out immediately. This 90-day roadmap reflects those patterns.

Section 1: Decoding the FAANG Evaluation Rubric

Most candidates think interviewers are checking if you drew the "correct" architecture. Wrong.

Here's the actual breakdown based on internal calibration discussions I've been part of:

Problem Scoping (20%): Did you ask clarifying questions? Did you define the scope before jumping into boxes and arrows? Candidates who immediately start drawing databases signal junior thinking.

Technical Depth (30%): Can you go deep when probed? When I ask "Why Kafka over SQS here?", do you have a real answer or just "Kafka is what I've seen used"?

Trade-off Analysis (25%): Every design decision has costs. Strong candidates articulate them unprompted. Weak candidates defend their choices without acknowledging alternatives.

Communication (15%): Can you explain your thinking clearly? Do you structure your presentation? Are you checking in with the interviewer?

Adaptability (10%): When I throw a curveball (scale 10x, add a new requirement), do you pivot gracefully or defend your original design?

Notice what's missing? "Correctness." There's no single right answer. I've seen candidates pass with wildly different architectures for the same problem because they demonstrated the right signals.

Section 2: The 90-Day Preparation Timeline

My system design interview study plan assumes you're working full-time and can dedicate 8–10 hours weekly. Adjust based on your schedule.

Weeks 1–4: Foundation Building

Goal: Build your mental toolkit of distributed systems concepts.

Week 1: CAP theorem, consistency models, database fundamentals (SQL vs. NoSQL trade-offs)
Week 2: Caching strategies, CDNs, load balancing approaches
Week 3: Message queues, event-driven architecture, pub/sub patterns
Week 4: Microservices patterns, API design, rate limiting

Reading about these isn't enough. For each concept, write a one-paragraph explanation in your own words. Can't explain it simply? You don't understand it.

Weeks 5–8: Pattern Recognition

Goal: Internalize common design patterns through active practice.

Practice designing these systems (45 minutes each, simulating interview conditions):

URL shortener (warm-up)
Rate limiter
News feed / Twitter timeline
Chat application
Notification system
Search autocomplete
Distributed cache

After each practice session, compare your design against two or three different resources. Note the variations. Those differences matter more than the similarities.

Weeks 9–11: Depth and Breadth

Goal: Develop expertise in specific areas while maintaining breadth.

Pick two or three areas to go deep:

Database internals (B-trees, LSM trees, indexing strategies)
Distributed consensus (Raft, Paxos basics)
Real-time systems (WebSockets, long polling, server-sent events)

Simultaneously, practice these more complex designs:

YouTube / Netflix video streaming
Uber / Lyft ride-matching
Distributed file system
Web crawler

Week 12: Integration and Mock Interviews

Goal: Full simulation under realistic conditions.

Do three or four mock interviews with real humans. Pay for them if necessary. The feedback is worth it. [Link: mock interview platforms comparison]

Review your weakest areas. No cramming new material at this point.

Section 3: The SPACE Framework

You have 35 minutes to design a system you'd spend months architecting in real life. You need structure. I developed this framework after noticing what distinguished passing candidates:

S – Scope and Scale

First 5 minutes. Clarify requirements. What's the read/write ratio? Peak traffic? Data retention needs? Geographic distribution?

Write these down visibly. It shows structure and gives you reference points.

P – Propose High-Level Design

Minutes 5–12. Draw the 10,000-foot view. Major components and how they connect. Save the deep stuff for later.

"Here's my initial approach. I'm proposing three main services: a user-facing API layer, a processing tier, and a data layer. Before I dig into each, does this align with what you're looking for?"

A – Articulate Component Details

Minutes 12–25. Now go deep on two or three components. Your interviewer will often guide you here. Show your technical depth.

This is where you discuss database schema, caching strategies, and specific technologies. Be ready to justify choices.

C – Consider Edge Cases and Failures

Minutes 25–30. What happens when things break? How do you handle network partitions? Hotspots? Data corruption?

Strong candidates bring this up themselves. Weak candidates only address failures when asked.

E – Extend and Evolve

Minutes 30–35. How would this scale 10x? What would you change? What monitoring would you add?

Leave time for interviewer questions. Been talking nonstop for 35 minutes? That's a red flag.

Section 4: Company-Specific Strategies

Here's where this system design interview prep guide for FAANG gets specific. Each company has its own culture.

How to Pass the Google System Design Interview

Google obsesses over scale. They'll push you on numbers. Can your design handle 1 billion users? What's the QPS? How many machines?

Come prepared with napkin math. Know that:

A single machine handles ~10K QPS for simple requests
RAM reads: ~100 nanoseconds
SSD reads: ~100 microseconds
Cross-region network latency varies significantly: ~20–50ms for nearby regions (e.g., US East to US West), ~70–150ms for cross-continental routes (e.g., US to Europe), and ~150–300ms for intercontinental routes (e.g., US to Asia-Pacific)

Practice doing quick calculations out loud. "If we have 100M DAU with an average of 10 requests each, that's 1B daily requests, roughly 12K QPS average, maybe 36K peak..."

System Design Interview Questions: Amazon and Meta

Amazon focuses on operational excellence. They'll ask: How do you deploy this? How do you handle regional failover? What alerts would you set up? Their leadership principles matter here. Show customer obsession and ownership.

Meta (Facebook) emphasizes product thinking and iteration. They want to see you balance engineering constraints with user experience. Expect more questions about privacy considerations and content policies than you'd get at other companies.

The Netflix / Stripe / Airbnb Approach

Interviewing at strong non-FAANG companies? The bar is often equally high but culturally different. Research their engineering blogs. Netflix publishes extensively about their architecture. Use their terminology when relevant.

Section 5: Solo Practice Methods That Actually Work

Finding mock interview partners is hard, and paid mocks are expensive. So how do you practice system design interviews alone?

The Whiteboard Recording Method

Set up a phone to record yourself at a whiteboard (or iPad). Explain your design out loud for 35 minutes, just as you would in an interview. Then watch the recording.

This is painful. You'll notice verbal tics, long pauses, and unclear explanations. That's the point.

The Blog Post Technique

After each practice problem, write a blog post explaining your design. Writing forces precision. You'll discover gaps in your thinking.

I used this approach when preparing for my Google interviews back in 2015. I never published most of them, but the writing itself was valuable.

The Architecture Review

Find real system design documents from companies. AWS and Google publish architecture case studies. Read them critically. What would you do differently? What trade-offs did they make?

The Teach-Back Method

Explain a concept to someone non-technical, whether that's your partner, a friend, or your rubber duck. Can't explain consistent hashing to a smart non-engineer? You don't truly understand it.

Section 6: Common System Design Mistakes to Avoid in Interviews

These five patterns instantly reveal unprepared candidates. I've seen each dozens of times.

Mistake 1: Starting to Draw Immediately

"Design Twitter." And the candidate immediately draws a user service, a tweet service, a database...

Stop. Ask questions first. Which features of Twitter? What scale? Is real-time timeline required, or is eventual consistency acceptable?

Candidates who clarify requirements signal senior thinking. Candidates who assume signal the opposite.

Mistake 2: The Résumé System

"I'd use Kafka because that's what we use at my current company."

Your experience is relevant, but you shouldn't just replicate your current system. Show that you understand why Kafka fits this particular problem, not just that you've used it before.

Mistake 3: Technology Name-Dropping Without Depth

"We'd use Redis for caching, Kafka for messaging, Elasticsearch for search, Cassandra for storage..."

Okay, but why? What are the trade-offs? When I probe, can you explain Redis's eviction policies? Kafka's partition strategy?

The best resources for learning system design for big tech emphasize depth over breadth. Know 5 technologies deeply rather than 15 superficially.

Mistake 4: Ignoring the Interviewer

Your interviewer says "Interesting, but what about consistency here?" and you respond "Yeah, so anyway, moving on to the caching layer..."

Pay attention. Those nudges are gifts. Your interviewer is pointing you toward something important, possibly something that's causing concern about your design.

Mistake 5: The Perfect System

Real systems have trade-offs. Your design has no downsides? You're either a genius or you're missing something. Guess which one the interviewer assumes.

Strong candidates say things like: "This approach prioritizes availability over consistency, which means we might show stale data for up to 30 seconds during partition events. For this use case, that's acceptable because..."

Okay, enough theory. Here's what to do this week:

Days 1–2: Read Designing Data-Intensive Applications chapters 1–2. Best ROI resource available.

Day 3: Practice designing a URL shortener in 35 minutes. Record yourself.

Day 4: Watch your recording. Write down three things to improve.

Days 5–6: Study caching strategies. Write a one-page summary.

Day 7: Practice a rate limiter design. Resist looking at solutions first.

Resources Ranked by ROI:

Designing Data-Intensive Applications by Martin Kleppmann (the bible)
ByteByteGo newsletter and YouTube channel (for visual learners)
System Design Interview books by Alex Xu (good pattern coverage)
Company engineering blogs (free and directly relevant)
Paid mock interviews (expensive but highest-signal feedback)

Here's the fundamental difference between system design interview preparation and coding interview preparation. Coding interviews test whether you can solve defined problems. System design interviews test whether you can define problems worth solving.

Start today. The 90 days go fast.