system-design

Big Tech Guide (Google, Meta, Amazon, Microsoft)

The big leagues. Big Tech interviews are the most formal, rubric-driven, and demanding — and also the most predictable, which means the most preparable. This guide covers what Google, Meta, Amazon, and Microsoft actually test, how their processes differ, and how to prepare for the bar that this entire repo is ultimately calibrated to.

Prerequisites: Company Tiers, The Framework, Rubrics Time to read: ~15 minutes

What Big Tech interviews look like

🚨 A full loop is typically 4–6 rounds: coding (2–3× DS&A), system design (1–2×, more for senior), behavioral (1×+), sometimes domain/LLD. System design weight increases with level — for a fresh grad it may be light or absent; for mid/senior it’s decisive.

The defining traits:

Formal rubrics. Interviewers score against explicit dimensions (see rubrics): requirements, scale/estimation, high-level design, deep dives, trade-offs, communication. 🚨 Hit every dimension — a great design that never estimates or never names a trade-off underscores.
Scale is real. Design for billions of users; know the scaling journey and distributed-systems fundamentals cold.
Trade-offs are everything. There’s no right answer; they grade your reasoning and justification.
Structured process. Standardized, calibrated, “hire/no-hire” with committee review at some companies.

Company-by-company

Google

System design: classic, scale-heavy (design a search-adjacent system, a KV store, a crawler, YouTube). Deep on distributed systems, data structures, algorithms within the design. Values clean reasoning and fundamentals.
Coding: strong DS&A, sometimes with a Google-y algorithmic flavor.
Behavioral: “Googleyness” — collaboration, humility, comfort with ambiguity.
Process: phone screen(s) → onsite (coding, design, behavioral) → hiring committee. Notoriously thorough; the committee reviews packets.
Prep: the framework + flagship case studies (search, crawler, KV store, YouTube) + deep distributed systems.

Meta (Facebook)

System design (“Product Architecture”): often product-flavored — design Instagram, a news feed, a messaging system, live comments. 🚨 Emphasis on feeds, social graph, real-time, and scale. Fast-paced; they want you to move quickly and cover a lot.
Coding: high-bar DS&A, fast.
Behavioral (“Jedi”): impact, ownership, conflict, moving fast.
Prep: Twitter, Instagram, news feed, chat, live comments — the social/real-time cluster.

Amazon

System design: scale + practical; often tied to Amazon-like problems (e-commerce, ordering, inventory). 🚨 Leadership Principles permeate every round — including design (they’ll ask about customer obsession, trade-offs, ownership in your design choices).
LLD/OOP design: Amazon frequently includes an object-oriented design round — prepare LLD deliberately.
Behavioral: the 16 Leadership Principles are central — have STAR stories mapped to them. This is often the hardest part to prepare and the most decisive.
Coding: solid DS&A.
Prep: e-commerce, payment, notification, LLD problems, and deep LP prep.

Microsoft

System design: solid, scale-aware, often practical and product/team-specific. Generally a bit less brutal than Google/Meta but still rigorous, varying by team.
Coding: strong DS&A.
Behavioral: growth mindset, collaboration.
Prep: the framework + core case studies; the specifics depend heavily on the team.

🚨 Netflix, Apple, Uber, Stripe, etc. each have their own flavor (Netflix: senior-heavy, freedom & responsibility; Stripe: payments/API depth; Uber: their domain). Research the specific one.

How to prepare for Big Tech

Master the framework. Non-negotiable — it’s how you hit every rubric dimension consistently.
Estimation must be fluent. Scale is real; drill estimation.
Deep distributed-systems knowledge. Part 4 — consistency, CAP, replication, consensus, sharding. Big Tech probes these.
Know the flagship case studies cold, weighted to your target (social for Meta, scale/search for Google, commerce/LP for Amazon).
Trade-off fluency. Narrate trade-offs constantly in trade-off vocabulary.
Behavioral is not optional. Especially Amazon LPs. Prepare STAR stories.
Coding in parallel. Design won’t save a failed DS&A round; keep LeetCode sharp.
Mock relentlessly. The formality rewards polished, structured performance — practice with mocks until the process is automatic.

The bar, honestly

🚨 Big Tech is preparable, not magic. The questions are largely known (the 36 case studies cover the vast majority), the rubric is explicit, and the process is standardized. What separates offers from rejections is fluency — clarifying, estimating, designing, and reasoning about trade-offs smoothly under pressure, having done it 30+ times in practice. Talent helps; deliberate, structured preparation decides.

For fresh grads: Big Tech new-grad roles lean more on coding + fundamentals than deep design (design ramps with level). Get the fundamentals and coding strong; design depth grows with your career. Many engineers reach Big Tech via a few years at a scale-up or local company first — a completely valid, common path.

🚨 Common Big Tech pitfalls

Missing a rubric dimension — a brilliant design that skips estimation or trade-offs underscores.
Under-preparing behavioral — especially Amazon LPs; it’s often the deciding round.
Generic prep across companies — Meta wants feeds, Amazon wants LP-infused commerce/LLD; target it.
Neglecting coding — the algorithms rounds still gate the loop.
Not mocking enough — the formal process rewards rehearsed fluency; winging it shows.
Freezing on scale — practice the 1→1B journey until it’s second nature.

🛠️ Try it

1. Target one company and mirror its loop. Pick Google/Meta/Amazon/MS, list its exact rounds, and build a prep plan matching its emphasis (Meta → social case studies; Amazon → LP stories + LLD).

2. Do a full rubric-graded mock. A 45-minute design with a partner who scores every rubric dimension. Fix your lowest dimension, repeat.

3. Write your STAR / LP stories. Draft 6–8 concrete behavioral stories mapped to leadership principles / values. For Amazon, map to the 16 LPs specifically. See behavioral.