FAANG Resume: What Big Tech Recruiters Screen For

A FAANG resume is a software engineer resume held to a stricter standard. The acronym (Meta, Amazon, Apple, Netflix, Google) has outlived Facebook’s old name, and people now use it for big tech in general, so a Google software engineer resume, a Microsoft one and a Stripe one face the same screen. Recruiters there work through long queues, so the first read is a skim, and the companies have published what they want that skim to find.

I have never worked in big tech recruiting, and this guide claims no insider knowledge. It uses what Google, Meta and Amazon publish on their own careers pages, plus the structure of a strong software engineer resume, and it gives you a fictional set of example bullets to measure yours against.

What Big Tech recruiters screen for, in their own words

Google’s hiring page calls your resume “our first look into your past experiences and impact” and tells you to start from a blank document for each role rather than editing an old file. The specific instructions on that page (Google Careers, How we hire):

• “Align your skills and experience with the job description. Tie your work directly to the role qualifications (and don’t forget to include data).”
• Make sure you meet the minimum qualifications, and make sure the resume shows it.
• “Be specific about projects you’ve worked on or managed. What was the outcome? How did you measure success?”
• “If you’ve had a leadership role, tell us about it. How big was the team? What was the scope of your work?”
• “Keep it short. We don’t have a length requirement, but being clear and succinct are key.”

Meta’s hiring-process page asks for a resume “formatted in a way that’s easy to read and emphasizes your work experiences, skills and achievements”, tells you to check the minimum qualifications before you apply, and notes that its careers site holds one resume per applicant (Meta Careers, Hiring process).

Amazon’s interview-loop page lists its Leadership Principles, behavioural questions and the STAR method under “what to expect and how to prepare” (Amazon Jobs, Interview loop). The principles themselves are public: “Deliver Results” asks leaders to “focus on the key inputs for their business and deliver them with the right quality and in a timely fashion”, and “Ownership” says “Leaders are owners” (Amazon, Leadership Principles). A resume bullet with a result and a measure doubles as the opening of a STAR answer in the interview.

Read those three pages together and the screen reduces to three tests for each bullet.

Scope

Scope is the size of the thing you worked on. Google asks for it outright in leadership roles (team size, scope of work), and the same question applies to code. Users served, requests a second, services owned, dollars processed, engineers on the team, months in production. A bullet without scope could describe a weekend project or a system that runs a company, and the recruiter cannot tell which.

Impact

Impact is the change that happened because you were there. “Worked on the search service” is a seat. “Cut search p95 latency from 600 ms to 180 ms” is an outcome. Big tech postings describe problems at a certain size, and the recruiter is matching your past outcomes against that size.

Metrics

Metrics are the measurement of that change. Google’s page says “don’t forget to include data” and asks how you measured success. A metric needs a baseline: “cut latency by 70%” means nothing without “from 600 ms”. Numbers come from dashboards, incident reports, A/B test results and sprint reviews. If you only have an estimate, write “about” and keep it defensible; the interviewer will ask how you got the number.

The XYZ formula for FAANG resume bullets

Google’s hiring page says: “When in doubt, lean on the formula, ‘accomplished [X] as measured by [Y], by doing [Z].’” Its interview-prep page repeats the same equation and adds that Google “loves data” (Google Careers, Interview prep). The formula predates the careers page: Laszlo Bock, who ran Google’s People Operations, published it in a 2014 LinkedIn post, with the advice to start with an active verb, measure the result in numbers, give a baseline for comparison, and describe what you did.

For engineering bullets, the three slots map like this:

• X, the accomplishment. The change in the system or the outcome for users: faster, cheaper, more reliable, launched, migrated, unblocked.
• Y, the measure. The number with its baseline: “from 14 hours to 25 minutes”, “for 30M monthly transactions”, “across 12 services”.
• Z, the method. What you built and with what: the design decision and the technologies. Z is also where the keywords live, so a parser finds “Kafka” and “Terraform” inside a sentence that proves you used them.

The order is flexible. Lead with X when the outcome is impressive, with Y when the scale is the story, with Z when the technique is what the posting asks for. The test is whether all three are present in the bullets that matter.

FAANG resume example bullets (fictional)

The engineer below is invented, along with the company and every number. Each pair shows a bullet as most people write it, then the same work rewritten with scope, impact and a measure.

Mateus Ferreira, Software Engineer, Tidewater Analytics (fictional), 5 years’ experience.

Before: “Worked on the data ingestion pipeline.”

After: “Rebuilt the ingestion pipeline from cron-driven batch jobs to a streaming design on Kafka and Flink, raising daily throughput from 40M to 310M events with no change in infrastructure cost.”

Before: “Improved API performance.”

After: “Cut p99 latency on the reporting API from 2.4 s to 380 ms for 1,900 enterprise customers by moving aggregation into pre-computed ClickHouse materialised views.”

Before: “Helped with on-call and reliability.”

After: “Owned on-call for 6 services with a combined 99.9% availability target; wrote the runbooks and alert rules that reduced pages per week from about 25 to 7 over two quarters.”

Before: “Mentored junior engineers.”

After: “Onboarded and mentored 3 new engineers, each shipping to production within their first 3 weeks; wrote the team’s code-review guidelines, now used by 4 teams.”

Before: “Led the migration to Kubernetes.”

After: “Led a 4-engineer migration of 22 services from VM-based deploys to Kubernetes on GKE over 7 months, with zero customer-facing incidents and deploy time down from 40 minutes to 6.”

Before: “Built an internal tool for the sales team.”

After: “Built a self-serve usage dashboard in React and FastAPI that replaced weekly manual exports for a 30-person sales team; adopted by all 30 within a month of launch.”

Two patterns to notice. The after-versions name the technology inside the sentence rather than in a list. And none of them claims a company-wide number; each one claims the part Mateus owned, with a verb (“rebuilt”, “owned”, “led”) that an interviewer can probe.

When you have no metric

Some work has no clean number: a refactor, a design review, a security fix that prevented an incident nobody saw. Give those bullets scope instead of a metric. “Refactored the authentication module (11k lines, 3 services) to remove a shared mutable session cache; the change shipped behind a flag with no regressions over 60 days.” Size, blast radius and duration are measurements too.

One page

Google says it has no length requirement and wants clear and succinct. One page is the convention for a big tech resume until about eight years of experience, and nothing on the companies’ pages argues for more. The second page exists for staff-level engineers with long histories, and even then the oldest roles compress to a line each.

To get to one page, cut in this order: the objective statement, the hobbies line, the oldest role’s bullets, projects that duplicate skills your jobs already prove, and any bullet that has no scope, impact or measure. Do not shrink the font below 10 points or the margins below half an inch to avoid these cuts; the recruiter notices a crammed page before she reads a word of it.

Skip the cover letter unless the form requires one. Google’s page says the company does not require them and suggests you spend the time on the resume.

Minimum qualifications and the one-resume rule

Both Google and Meta tell you to check the minimum qualifications before applying and to make sure the resume shows you meet them. Treat the minimum qualifications as a checklist: for each line, point to the bullet or the skills entry that proves it. If a required item is missing from your resume and true of your experience, add it. If it is missing from your experience, apply anyway only when the gap is one item and the rest is strong.

Google lets you apply for up to three jobs every 30 days, according to its hiring page, so pick the three that fit rather than spraying. Meta keeps one resume per applicant on its careers site, which means your Meta resume has to cover every Meta role you apply to at once; write it for the strongest overlap between those roles rather than for one of them. For companies that take a fresh file per application, tailor each one. The method is in our guide to tailoring a resume to a job description.

How to handle referrals

A referral puts your resume in front of a recruiter through an employee’s submission rather than the general queue. It does not change what the resume has to prove, and it does not skip the screen or the interviews. Treat it as a way to get the file read, and make sure the file is ready before you ask.

• Ask someone who has seen your work. Referral forms often ask the employee how they know you and how your work compares with peers. A former colleague or an open-source collaborator can answer that; a stranger from LinkedIn cannot.
• Make it easy. Send the job ID, the tailored PDF, and three lines on why you fit the specific role. The referrer pastes those into the form.
• Apply yourself as well. Submit the same resume through the careers site so your application exists even if the referral is delayed, and tell the referrer you have done so.
• One ask per company. Multiple referrals for the same role do not stack, and asking five people signals that you are collecting them.
• Thank the person whatever happens. Referrals cost the employee time and a small amount of reputation.

Mistakes that stand out on a FAANG resume

The team’s numbers as your own. “Grew revenue 40%” from an engineer who built one feature invites an interview question that ends the conversation. Claim your part with the right verb.

Vague verbs. “Helped”, “participated in”, “was involved with” tell the recruiter you were present. “Built”, “owned”, “led”, “designed” tell her what you did.

Internal names without translation. “Migrated Falcon to Hydra” means nothing outside your old company. “Migrated the order-matching service to the new event bus” means something anywhere.

A forty-item skills list. It makes the recruiter guess which ten you can discuss. Keep the tools you could be interviewed on this month.

Prestige in place of evidence. A recognisable company name on the resume helps, and it does not replace scope and metrics for the work you did there.

Buzzwords. “Passionate”, “results-driven”, “cutting-edge”, “synergy”. One page holds a limited number of lines; spend them on facts.

Design over parsing. Two-column layouts with icons, skill bars and a photo look impressive and parse into noise. Big tech applications go through an applicant tracking system before a person sees them; a single-column PDF with real text and standard headings survives the trip. Our resume keywords guide covers the terms a parser looks for.

Build the rest of the resume

The bullets carry a FAANG resume, but the summary, skills, projects and education sections still have to be right, and the formatting has to survive an ATS. The software engineer resume guide has a copyable one-page template, two fictional full examples at mid and senior level, and section-by-section advice; this post is the stricter bar to apply on top of it.

If you are applying to several big tech companies at once, each with its own minimum qualifications, Resume Matcher, the free open-source tool I maintain, keeps a master resume and cuts a tailored version for each posting. You run it on your own machine with the AI model you already use, and code checks lock your employers, titles and dates and revert any number the model tries to invent, which matters more here than anywhere: a fabricated metric on a big tech resume surfaces in the first technical interview.

If this helped

The companies have told you what they look for; the work is writing bullets that answer them. If this guide or the tool helped, a star on Resume Matcher on GitHub helps other engineers find it, and you can follow me, Saurabh Rai, on GitHub, X and LinkedIn.