▸ COMPANY TRACKS · per-employer prep

Company tracks — targeted interview prep.

Generic FAANG prep gets you to the loop; company-specific prep gets you the offer. Each track below maps the loop format, the bar, the cultural signals, the specific question banks, and the rounds the company is known to weight unusually. Updated for the 2026 interview cycle.

Tracks

  1. Google
  2. Meta
  3. Amazon
  4. Netflix
  5. Databricks
  6. Snowflake
  7. Microsoft
  8. OpenAI & Anthropic
Track · Search · Cloud · YouTube

Google

Loop format

  • Phone screen — 1 round, algorithms (45 min)
  • Onsite — 5 rounds: 2 coding, 1 system design, 1 Googliness/Leadership, 1 domain (DE/ML/distributed)
  • L5+ adds a "General Cognitive Ability" round — ambiguous open-ended problem

The bar

  • Coding — clean, optimal, with edge cases. Whiteboard or Google Docs (no autocomplete).
  • System design — Google-scale framing. Petabyte data, billion-user reasoning required.
  • Googliness — comfort with ambiguity, intellectual humility, change-mind-on-data.

What's specific to Google

  • Hiring committee separates from interviewers; interviewers don't decide. Your written packet matters.
  • HC bias toward depth — solve one hard problem completely, not three half-way.
  • Levels are sticky — they pattern-match to internal definitions; SWE III (L4) vs Senior SWE (L5) vs Staff (L6) bar each has 3-4 specific signals.

Practice question pack

Track · Facebook · Instagram · WhatsApp · Reality Labs

Meta

Loop format

  • Phone screen — 1 round, coding
  • Onsite "full loop" — 5 rounds: 2 coding, 1 system design (or ML system design for AI roles), 1 behavioral, 1 career story / cross-functional
  • Coding rounds are typically 2 problems each — pace matters

The bar

  • Coding — speed + correctness. Two problems in 45 minutes. No dwelling.
  • System design — feed, messaging, distributed systems at billion-user scale.
  • Behavioral — every claim quantified. "Engagement +X% over Y days." No softness.

What's specific to Meta

  • Move Fast — but quality preserved. Speed alone reads as careless.
  • Impact = metrics + scope. The career story must trace metrics moved across roles.
  • Cross-functional partnership — every story should include a non-engineer partner.

Practice question pack

Track · Retail · AWS · Devices

Amazon

Loop format

  • Online assessment (OA) — 2 coding problems + work simulation + LP questionnaire
  • Phone screen — 1-2 rounds, coding + LP
  • Onsite — 4-6 rounds, each ~50% LP / 50% technical. Includes a Bar Raiser round.

The bar

  • 16 Leadership Principles — see the Behavioral page
  • Bar Raiser veto — one out-of-org interviewer can sink the offer. They focus on LP depth.
  • Frugality runs through everything — "do with less" stories prized.

What's specific to Amazon

  • Every round is LP-driven — even coding interviewers will ask 2-3 LPs. Don't be surprised.
  • "Tell me about a time" — every behavioral question is LP-flagged. Listen for the LP, then map to your prepared story.
  • STAR rigor — Amazon interviewers will interrupt to ask "what was YOUR specific action?" Be ready.

Practice question pack

Track · Content · Streaming Tech · Studios

Netflix

Loop format

  • Recruiter screen — culture fit + leveling probe
  • Manager screen — domain expertise + culture deep-dive (Netflix culture memo is required reading)
  • Onsite — 4-5 rounds: deep technical, system design, behavioral with bar, business judgment

The bar

  • Senior individual contributors only — Netflix hires at L5+ ("Senior" is the entry-level for engineers).
  • Keeper test — would the manager fight to keep you?
  • Context, not control — every answer should show you make decisions with autonomy.

What's specific to Netflix

  • Culture memo is the rubric. Re-read it before every loop.
  • No PMs at low scopes — engineers own product. Stories about "I waited for the PM" are red flags.
  • Compensation — top-of-market all cash, no stock vesting. Negotiate hard once.

Practice question pack

Track · Lakehouse · Spark · MosaicML

Databricks

Loop format

  • Recruiter + HM screen
  • Take-home or technical screen (Spark / dbt / data modeling)
  • Onsite — 4-5 rounds: 2 coding (often PySpark), 1 system design, 1 behavioral, 1 domain

The bar

  • PySpark live-coding — DataFrame API fluency, joins, window functions, optimization (broadcast, salting, AQE)
  • Lakehouse architecture — Delta Lake internals, Unity Catalog, table format choices
  • Founder mindset — "what would you build?" is a real interview question

What's specific to Databricks

  • Customer obsession meets open source pragmatism — name OSS communities you're part of.
  • The Spark expert round — they'll dig deep on shuffles, AQE, broadcast, cache, partition strategy.
  • Hands-on bar — they verify with live coding what you claim on your resume.

Practice question pack

Track · Cloud DW · Snowpark · Iceberg

Snowflake

Loop format

  • Recruiter + HM screen
  • Technical screen — SQL deep-dive, query optimization, warehouse internals
  • Onsite — 4-5 rounds: 2 SQL, 1 system design (lakehouse + governance), 1 behavioral, 1 product

The bar

  • SQL fluency — at the AQE / micro-partition / clustering-key level
  • Cost / performance trade-offs — warehouse sizing, query cost attribution
  • Customer empathy — you're often interfacing with customer data engineers

What's specific to Snowflake

  • Micro-partition internals come up — clustering keys, search optimization, materialised views.
  • Cost optimization as a craft — Snowflake's product IS cost; they expect you to think about it.
  • Multi-cloud awareness — AWS / Azure / GCP all matter.

Practice question pack

Track · Azure · M365 · GitHub · Copilot

Microsoft

Loop format

  • Recruiter screen
  • Phone interview — 1-2 rounds, coding + design
  • Onsite — 4-5 rounds: 2 coding, 1 system design, 1 hiring manager, 1 "As Appropriate" (AA, the final say)

The bar

  • Coding — solid, not maximally clever; readable code preferred
  • Design — Azure-flavoured at scale; expect questions about specific Azure services
  • Growth mindset — Satya Nadella era cultural baseline

What's specific to Microsoft

  • "As Appropriate" round — one senior interviewer has final sign-off. Treat it like a sanity check, not a separate bar.
  • Domain match — match yourself to the right team early; orgs are very different at Microsoft.
  • Growth mindset stories — pure technical brilliance with bad collaboration won't pass.

Practice question pack

Track · OpenAI · Anthropic · frontier AI labs

OpenAI & Anthropic

Loop format

  • Recruiter + HM screen
  • Technical screen — usually a real coding problem (not LeetCode); often pair-programming
  • Take-home or design exercise
  • Onsite — 4-5 rounds: 2 coding, 1 system design (AI-flavoured), 1-2 mission/values, 1 domain

The bar

  • Coding — clean, tested, refactored. They watch HOW you code.
  • System design — RAG / agents / model-serving infra. See the AI Engineering page.
  • Mission alignment — can you articulate, specifically, why THIS lab matters?

What's specific to frontier labs

  • Mission round — Anthropic's is explicit about safety; OpenAI's about AGI capabilities. Don't be generic.
  • Compensation — top of market; both labs negotiate well for strong candidates.
  • Bar is high on theory + practice — you need to talk both about ML internals AND production engineering.

Practice question pack

  • The AI Engineering design page is the curriculum
  • 5 OpenAI Python questions in the bank · 3 Anthropic
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Cross-cutting interview prep

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