GCP Data Engineer Resume Examples & Writing Guide for 2025

As a GCP data engineer, you're sitting in the driver's seat of one of tech's hottest fields. Companies across advertising, fintech, retail, and SaaS are actively hiring professionals who can wrangle hundreds of terabytes—or even petabytes—of data using Google Cloud's powerful ecosystem.

However, in the high-stakes world of Big Data and stream processing, every mistake can cost businesses millions. That's exactly why employers set the bar so high when it comes to experience and technical proficiency.

So what does this mean for you? Even if you've got all the right skills, and you're perfect for the role, you might still find yourself getting passed over without even landing an interview. The problem isn't your lack of experience—it's how you're presenting it in a data engineering resume.

When hiring managers search for a GCP data engineer (especially one with Big Data chops), they're hunting for the candidate—someone who's already solved real-world problems with their specific tech stack and can hit the ground running from day one. Send them a generic, templated resume when hundreds of other candidates are applying? It’ll almost certainly end up in the “no” pile.

The good news: even entry-level data engineers have a real shot at standing out. You can land that interview if you demonstrate your ability to tackle typical challenges—even if that experience comes from internships, personal projects, or certification labs.

The Questions That Will Make or Break Your Application

✓ How to showcase both the breadth and depth of your Google Cloud experience so you grab a recruiter’s attention in those crucial first six seconds

✓ How to prove your business value and show that you can solve real-world problems with GCP—not just list technologies you’ve touched

✓ How to structure a GCP data engineer resume to get past ATS filters and land in front of real decision-makers

In this comprehensive guide, you'll discover the answers to these critical questions, plus:

● Real-world GCP Data Engineer resume examples that actually work

● Side-by-side analysis of strong vs. weak resume statements

● Expert advice from industry leaders who've been in the hiring trenches

● Actionable strategies to craft a resume that works for both entry-level engineers and seasoned professionals

Don't let another opportunity slip through your fingers. This guide contains everything you need to transform a GCP data engineer resume into a powerful marketing tool that showcases your data engineering skills, proves your value, and dramatically increases your chances of landing the next GCP data engineering role.

Ready to turn your resume into your secret weapon? Let's dive in.

Whether you follow this guide step by step or fast-track with our builder, you’ll end up with a professional resume that gets noticed—and gets interviews.

Data engineers build and maintain systems that collect massive amounts of data from various sources, store it, and transform it into something analysts and managers can actually use. But GCP data engineers? They specialize in designing and implementing these systems using Google Cloud Platform's powerful ecosystem.

Google Cloud tools shine when performance matters, when scaling has to happen without dismantling the tech stack, and when effortless integration with other Google services is expected.

The platform works just as well for scrappy startups as it does for enterprise projects where data volumes can explode by 10x overnight. Services like BigQuery, Looker, DataFlow, BigLake, and Cloud SQL are the go-to choices for financial companies and banks, e-commerce giants, SaaS platforms, streaming services, ad exchanges, mobile and online game publishers, social media platforms… the list goes on.

Why GCP Dominates (And Why It Matters for a GCP Data Engineer Resume)

✓ Flexible Scaling — Need to triple storage capacity or scale down during quiet periods? A few clicks are all it takes. The pay-as-you-go model prevents wasted spend during unpredictable data flows.

✓ Unmatched Performance — BigQuery isn't just fast; it's ridiculously fast. We're talking about one of the fastest analytical data warehouses on the planet. When Big Data performance matters, Google delivers.

✓ AI/ML Integration — Google’s AI ecosystem (Vertex AI, AutoML) isn’t an afterthought—it’s built right in. Gemini-powered models can be applied directly without complex integration steps.

✓ Enterprise-Grade Security — IAM, encryption, data loss prevention—it's all there out of the box, not as expensive add-ons.

Here's Where Most Resumes Fall Flat

I'm sure you know Google Cloud's advantages better than I do. This leads to a critical question: how does this translate to a resume that actually gets you hired?

Typical responsibilities like designing and maintaining ETL/ELT pipelines, building analytical data warehouses, consolidating data streams from multiple sources into data lakes, and collaborating with analysts and data scientists—these are table stakes. Recruiters expect every candidate at your level to have those.

When employers hunt for a GCP specialist, they need someone who doesn't just use the tools, but maximizes their potential. They want someone who can squeeze every ounce of performance, cost savings, and business value from the platform.

What Recruiters Really Want to See in a GCP Data Engineer Resume

When HR managers scan a GCP Data Engineer resume, they're looking for four critical elements:

✓ Problem-Solving Prowess — Can you tackle real business challenges using Google Cloud's data stack? Show them you're not just a tool user, but a solution architect.

✓ Scale & BigData Chops — What's the volume of data you've wrangled? How many sources have you unified? Prove you can build scalable cloud storage, pipelines that won't buckle under pressure.

✓ Measurable Business Impact — This is where many resumes die. How did your work move the needle? Did you slash infrastructure costs? Boost ad campaign ROI? Enable game-changing business decisions? Numbers tell the story.

✓ Leadership & Innovation — Are you the data engineer who improves what’s already working? Makes tools faster, more accurate, more user-friendly? Highlight it. For senior roles, employers are also looking for mentoring experience and cross-functional collaboration skills.

Key Skills to Highlight in a GCP Data Engineer Resume

When building the resume skills section, highlight the tools and capabilities used to demonstrate the ability to architect scalable data solutions in the Google Cloud ecosystem.

Core Technical Skills for GCP Data Engineer Resume

Data Processing & Orchestration

● BigQuery — data warehousing, query optimization, cost management

● Dataflow (Apache Beam) — real-time and batch data processing

● Dataproc — managed Hadoop/Spark clusters

● Apache Airflow / Cloud Composer — workflow orchestration for ETL/ELT

● dbt — data transformation, modeling, dependency management

Programming & Development

● SQL — advanced query optimization, window functions, CTEs

● Python — pandas, NumPy, building data pipelines

● Java / Scala — development for Spark/Beam applications

Cloud Infrastructure & DevOps

● Cloud Storage — data lake architecture, object lifecycle management

● Pub/Sub — event-driven architecture, real-time messaging

● Cloud Functions — serverless event and data processing

● IAM — security and access control

● Terraform / Deployment Manager — infrastructure as code

● Kubernetes / GKE — containerized workloads in the cloud

Analytics & Visualization

● Looker — BI analytics, LookML data modeling

● Data Studio / Tableau / Power BI — dashboards and visualizations

● BigQuery ML — in-database machine learning

Emerging Technologies

● Vertex AI — ML model deployment and lifecycle management

● BigLake — unified analytics across data lakes and warehouses

● Dataform — advanced data transformation workflows

Critical Soft Skills for a Data Engineer Resume

While technical skills open the door, personal skills drive long-term success:

● Stakeholder Translation — Converting business requirements into technical solutions that non-technical teams can understand

● Performance Optimization — Identifying bottlenecks and implementing solutions that save both time and money

● Cross-Functional Teams Collaboration — Working effectively with data scientists, analysts, and business stakeholders who have different priorities

● Project Management and Leadership — Driving data initiatives from conception to deployment, especially for senior roles

In fact, there’s no official “data engineer resume template” in any handbook. You have the freedom to tell your career story your way.

But—and this is a big but—recruiters still have clear expectations. They'll spend maybe 6–10 seconds on the first resume scan, and both the ATS and the human reviewer need to spot the essentials instantly. That's why your resume layout has to do the heavy lifting fast.

Your mission? Make it effortless for both the ATS and the human reviewing your application. Organize everything so your strongest skills and achievements jump off the page right away and spark interest to keep reading.

Here’s the GCP data engineer resume structure that consistently works:

1. Header & Contact Information

Include in resume: Full Name, City/State, Email, Phone, LinkedIn, GitHub or Portfolio site (optional)

Example:

John Doe

Senior Data Engineer: Retail, Google Cloud Platform

Austin, TX | JohnDoe_Data_Eng@mail.com | +1 372 123 45 67 | linkedIn.com/in/John-dataengineer

2. Professional Summary

A short, sharp 3–5 line intro that highlights experience, core stack, measurable outcomes, your business impact. The goal of the Professional Summary is to be a powerful hook. Recruiters should be able to read this section and immediately understand who you are, what you've done, and what value you can bring to their team.

A Summary Example for a Data Engineer Resume:

GCP Data Engineer with 5+ years of experience building scalable cloud data pipelines and analytical warehouses. Expert in BigQuery, Dataflow, Looker. Optimized ETL processes to cut query costs by 45%, accelerating data delivery for analytics, data science teams, and business decision-making.

3. Technical Skills

List your core tech stack (skills, tools, technologies) without explanations (save the proof for your experience section). Break it down into logical categories for easy scanning: Cloud Platforms, Data Tools, Programming Languages, BI & Analytics, Compliance & Security, etc.

4. Professional Experience

Show the career story in a reverse chronological order (most recent first). For each role, make sure to include: job title, company name + location (City, State), employment dates, 2–5 powerful bullet points describing your impact.

Golden Formula: write each bullet point using this framework: Challenge/Problem — Tool/Skill — Measurable Outcomes. This shifts the focus from listing tasks to highlighting achievements, keeps the experience results-driven, and clearly demonstrates Google Cloud expertise.

Example for experience bullet point:

“Optimized BigQuery pipelines, cutting query costs by 45% and reducing refresh times from 20 minutes to under 5 minutes.”

5. Projects (Optional).

Have impressive personal projects, volunteer work, or side hustles? Showcase them here.

Include: Project name & type, Specific role, Objectives you set, Results you achieved

6. Education

For mid-level & senior GCP data engineers: Keep it concise—list the degree, specialization, university, and graduation year. If you hold multiple degrees, lead with the one most relevant to data engineering. Highlight honors, awards, or standout research only if they reinforce expertise or initiative.

For entry-level & junior data engineers: Give this resume section more weight. Include relevant coursework, capstone projects, or thesis work that demonstrates technical skills. Even strong class projects, hackathons, or competitions can highlight readiness to tackle real-world data engineering challenges.

7. Certifications

Lead with Google Cloud certifications (e.g., Professional Data Engineer) — these carry the most weight. Then list other cloud credentials like AWS or Azure. Always include the year earned to show recency.

8. Achievements & Awards (Optional)

If you’ve received industry recognition, company awards, or standout accomplishments, add them here. This section helps you prove you can go beyond day-to-day tasks and deliver exceptional results.

9. Soft Skills (Optional)

Collaboration, communication, and problem-solving matter—especially when working with analysts, PMs, or non-technical stakeholders. If your data engineer resume is already dense, you can merge soft skills with technical skills in one section.

The Flexibility Factor

This structure is ATS-tested and recruiter-approved, but you're not locked into it. Minor tweaks won't break ATS scanning—feel free to add sections to a data engineer resume for volunteer work, patents, or swap education and experience if you're just starting out.

The key principle: Whatever structure you choose, make sure a recruiter can find your strengths in those critical first 10 seconds.

GCP Data Engineer Resume Example

Sure, having the right structure matters—it gets you past the ATS robots and makes recruiters' lives easier. But structure alone won't make you stand out from the pile of hundreds of other applications.

The real differentiator? Content that proves you're not just another resume in the stack.

Want to know how to write a resume that grabs an employer's attention and lands you that interview? Here are the insider tips that work for everyone—from industry veterans to fresh graduates just getting started.

Start With Intelligence Gathering

Before you write a single word in resume, do your homework. Spend 15–20 minutes researching the company: browse their website, check out recent projects, and understand their goals and challenges. You can even ask AI tools for quick insights.

Why it matters: When you know what keeps the company up at night, you can position your experience as the solution to their specific problems. Generic applications get generic responses—but targeted ones get interviews.

Beat the ATS Robot (It's Easier Than You Think)

Think recruiters are the first to read your resume? Nope. In most cases, it’s an ATS “robot” standing between you and the interview. These systems scan for the right keywords and formats—and if you don’t match, you’re out before a human ever sees your name. The good news? Outsmarting the bot is way easier than you think.

ATS survival kit for a standout Data Engineer Resume:

✓ Keep the design clean and simple: One clean column, no fancy graphics, no creative fonts. Stick to classics like Arial, Calibri, or Times New Roman.

✓ Speak their language: Copy keywords straight from the job description—tools, technologies, common tasks, and action verbs like optimized, automated, improved, implemented.

✓ Scatter strategically: Don’t just dump keywords in one place. Spread them across your summary, skills, experience, projects, and certifications.

✓ Stay real: Don’t keyword-stuff or list tools where you have only superficial experience. Keep the spotlight on your GCP expertise, with a few extras only if they truly fit.

Do this right, and the “robot” becomes your ally—clearing the way, so a real recruiter can see what you bring to the table.

Nail Your Contact Section (Yes, People Still Mess This Up)

You’d be surprised how many resumes fail before recruiters even glance at the experience—and often the reason is the contact section. If it’s unclear or incomplete, you risk losing attention before your experience even gets noticed.

✅ What to Include

✓ Full name + specialization. Add your role right under your full name: Senior GCP Data Engineer looks way better than just “John Doe”.

✓ Professional email. Something clean, like firstname.lastname@gmail.com. not macho97@... or kitty-ann@...

✓ Essential links only. LinkedIn, GitHub, or a portfolio site. No Facebook, no TikTok.

✓ Location (if required). City + state is plenty. Nobody needs your street address.

❌ What to Avoid in Data Engineer Resume

✘ TMI. Marital status, number of kids, or your home address.

✘ Wrong specialization. Don’t confuse recruiters by presenting yourself as “Python Developer” if you’re applying for a GCP role.

✘ Visual clutter. Skip photos, emojis, or decorative graphics. ATS hates them, recruiters don’t care.

Pro tip: Keep it clean. Stick with a simple, single-column header. The fancier the design, the higher the chance the ATS mangles it. Content, not colors, is what gets you noticed.

Career Overview (Summary): Your Chance to Hook a Recruiter in 5 Seconds

Some resume templates either bury the summary at the bottom or skip it completely. Big mistake.

Here’s a reality check: recruiters don't read resumes, they skim them. And your summary is the very first thing their eyes hit. You've got maybe five seconds to earn another 20 seconds of attention—or to get tossed into the “no” pile.

Think of your summary as the movie trailer of your career. It’s not the full story—it’s the sizzle reel. Short, punchy, and enough to make them want to see more. Skip it or bury it at the bottom, and you're leaving the best hook on the cutting room floor.

This resume summary is too generic. Could've been written by ChatGPT on autopilot. No spark, no outcomes, nothing that says you actually move the needle. And recruiters? They've seen a hundred just like it every week—and forget them in five seconds.

✅ The Formula That Works

Role + Years of Experience + Core Tools + Measurable Outcomes. That's it.

Two to five sentences (20–60 words), tuned to the job you want.

Why These WorkWhy These Work

● Open with the role and seniority, providing instant clarity.

● Include core tech keywords right at the start, benefiting both ATS and recruiters.

● Highlight measurable outcomes with hard numbers, such as dollars saved or performance improvements.

● Mention relevant certifications that carry real industry weight.

Bottom line: Your summary is your elevator pitch in text form. Nail it, and you buy yourself more time—the most valuable currency in resume reviews.

Show Your Technical Skills in Action (Not Just on Paper)

Listing tools on your resume without context is like showing up to an interview in a tuxedo and never opening your mouth. You look prepared, but can you actually deliver?



Recruiters don’t care that you’ve “used BigQuery.” They care that you’ve used it to solve real problems, save serious money, and make systems run faster.

Think of it this way: dropping tool names is like listing the ingredients in your fridge. Cool — but what can you cook with them?

That line could sit on a thousand resumes. No spark. No outcomes. Zero business value.

See the difference? This example shows:

● Context: a petabyte-scale migration, not a toy project

● Tech in play: the actual stack used

● Measurable outcomes: cost reduction & performance boost

● Business impact: $1.2M saved — a number that grabs attention.

Key takeaway: don’t just name-drop tools. Show how you’ve turned them into measurable impact. That’s what separates a keyword-stuffed resume from one that wins interviews.

Prove Your Business Value (This Is Where Most Resumes Die)

The biggest mistake data engineers make? Describing responsibilities instead of demonstrating results. Recruiters don't want to know what you were supposed to do—they want to know what you actually accomplished.

The Three Pillars of Demonstrating Value:

1. Use Power Verbs

● ❌ Weak: “Worked with GCP tools”

● ✅ Strong: “Optimized data warehouse using BigQuery” or “Implemented ETL pipeline using Pub/Sub + Dataflow stack, integrating 10 data sources”

2. Quantify Everything

Numbers make your achievements tangible and prove business impact:

● How much routine work did you eliminate for analysts?

● What infrastructure costs did you slash?

● What's the scale of your projects (data sources, processing volume)?

● How did your solutions boost advertising efficiency or drive sales?

3. Provide Business Context

Tell the complete story in a few words:

● What problem existed?

● What was your goal?

● Who benefited from your solution?

● How did your work impact the company?

✅ Perfect example: “Optimized ETL pipeline for Pub/Sub event processing, reducing BigQuery data delivery latency from 15 minutes to 2 minutes, enabling the marketing team to access near real-time analytics for campaign optimization.”

This shows technical execution & business impact—exactly what recruiters want to see.

Too many engineers treat the Education section as a throwaway line at the bottom of the page. But if you use it strategically, it can either reinforce your expertise (for seniors) or fill in the gaps (for juniors and career switchers).

For Mid- & Senior-Level Engineers

Keep it lean. Hiring managers don’t need a list of every class you took a decade ago—they want proof of your academic foundation and maybe one standout achievement. For mid- and senior-level data engineers, simply listing your degree, university, and graduation year is often enough. You can optionally include honors, research projects, or other notable accomplishments that highlight your initiative, drive, and technical preparation.

Short, sharp, relevant. That’s all you require.

For Junior Engineers (or Limited Experience)

Here’s where education becomes your secret weapon. Don’t just list your degree—showcase the projects that prove you can do the job even without years of professional experience.

Include things like:

● Relevant coursework (Data Warehousing, Internet Security, SQL Optimization)

● Capstone or thesis projects

● Hackathons & student competitions

● Open-source or indie projects

Pro Tip. If your experience section is thin, flip the order: put Education, Certifications, and Projects before your work history. That way, recruiters see your most relevant skills upfront, instead of buried at the end.

Certifications tell recruiters you invest your own time in staying current—and that's worth its weight in gold.

What to include:

● Certification name

● Issuing organization

● Year earned (or date range for lengthy programs)

Strategic ordering: Lead with GCP certifications, then add other cloud platforms at the end. Keep it current. Focus on certifications from the last five years. Older ones can usually be dropped unless they're particularly prestigious or relevant.

Example:

● Google Cloud Professional Data Engineer (2024)

● Google Cloud Professional Cloud Architect (2023)

● AWS Certified Solutions Architect (2022)

Even experienced engineers make these mistakes—and they're costing you interviews. Here's a checklist of common pitfalls and exactly how to fix them.

1. Listing Responsibilities Instead of Results

The Problem: Employers don't want a job description. They want proof you can deliver impact—how you solved problems and drove results.

❌ Resume killer: “Responsible for ETL pipelines.”

✅ Interview winner: “Built ETL pipelines in Dataflow that reduced data delivery time from 15 minutes to 2 minutes.”

The fix: Always lead with what you accomplished, not what you were assigned to do.

2. Missing Metrics and Scope

The Problem: Without numbers, your achievements are just claims. Metrics prove business value.

❌ “Improved performance of BigQuery queries”

✅ “Optimized BigQuery queries, cutting monthly processing costs by 30% and reducing runtime from 90s to 15s”

The fix: Each bullet should have at least one measurable impact—time saved, costs cut, performance gains, or data scale.

3. Overly Complex Layout or Flashy Design

The Problem: Fancy templates often break ATS parsing, meaning your resume never reaches human eyes.

❌ Two-column templates, emojis, charts, “creative” section names like “Career Story” or “Tech Wizard Skills”

✅ Single-column template with standard sections (Contact, Skills, Experience, Education) and business fonts (Arial, Calibri, Times New Roman)

The fix: Boring beats broken. ATS systems love simplicity.

4. Overstuffed Skills Section

The Problem: A 30+ tool dump looks like keyword stuffing. Recruiters want focus, not desperation.

❌ “Python, Java, Scala, R, Go, C++, Hadoop, Spark, Kafka, Azure, AWS, Docker, Kubernetes…”

✅ “BigQuery, Dataflow (Apache Beam), Pub/Sub, Cloud Storage, Cloud Composer, SQL, Python, Terraform”

The fix: Cap it at 15–20 core skills relevant to GCP. Quality over quantity always wins.

5. Generic Wording Without GCP Context

The Problem: Vague descriptions without specific GCP tools look irrelevant to the role.

❌ “Worked with data pipelines and SQL”

✅ “Designed ELT pipelines in Dataflow, optimized queries in BigQuery, integrated Pub/Sub for real-time streaming”

The fix: Always name specific GCP tools and explain how you used them.

6. Mixing Roles & Irrelevant Tech

The Problem: Blurring lines between Data Engineer, Analyst, and ML Engineer roles—or emphasizing AWS/Azure over GCP—confuses your positioning.

❌ "Built dashboards and performed statistical analysis"

✅ "Delivered clean, analysis-ready datasets to BI teams through Cloud Composer-orchestrated pipelines"

The fix: Stay in your lane. Focus on data engineering tasks using GCP tools.

7. Ignoring Cost Optimization

The Problem: Cost optimization for data infrastructure is a core KPI for every GCP data engineer. Skipping this leaves value on the table.

❌ "Migrated data to BigQuery"

✅ "Migrated 5TB on-premises warehouse to BigQuery, reducing storage costs by 45% while improving query performance 3x"

The fix: Always highlight cost savings, efficiency gains, and resource optimization wins.

Strong GCP Data Engineer Resume Examples

Check out these real-world GCP Data Engineer resume examples built on the best practices from this guide. See how top candidates showcase technical skills, measurable impact, and business value—so your resume doesn’t just list tools, it proves results and gets interviews.



Resume Example #1 Entry-Level GCP Data Engineer (Retail Focus)

Resume Example #2: Mid-Level GCP Data Engineer (Real-Time & ML Integration)

Resume Example #3: Senior GCP Data Engineer (FinTech Focus)

1. Google Cloud Professional Data Engineer

Core certification for GCP Data Engineers.

Validates your ability to design and build data pipelines, data warehouses (BigQuery), real-time streaming (Pub/Sub, Dataflow), and work with ML integrations (Vertex AI). Must-have for any resume.

2. Google Cloud Professional Cloud Architect

Provides a broader scope: cloud system design, security, and scalability. Valuable for Data Engineers aiming to grow toward Lead or Architect roles.

3. Google Cloud Professional Machine Learning Engineer

Useful for Data Engineers working at the intersection of data engineering + ML. Shows knowledge of Vertex AI, BigQuery ML, AutoML, and feature engineering for ML models.

4. Google Cloud Associate Cloud Engineer

Entry-level certification: deploying applications, managing services, monitoring.

Good for junior engineers or those just starting with GCP, though less valued than professional-level certs.

5. Google Cloud Professional DevOps Engineer

Relevant if the Data Engineer works actively with CI/CD, Terraform, Cloud Build, Kubernetes (GKE). Strengthens your profile in DataOps practices.

6. Google Cloud Professional Cloud Security Engineer

Key for GCP data engineers responsible for security and compliance (GDPR, HIPAA, PCI DSS). Highly useful in Fintech, banking, and health tech environments.

Your GCP Data Engineer resume is more than a list of tools—it’s your personal business case for why you should be hired. The best resumes blend technical expertise, measurable outcomes, and real business impact to stand out in a stack of applications.

Now it’s your turn: take the examples, adapt them to your story, and showcase tech skills with clear metrics, quantified wins, and the right structure. Whether you follow this guide step by step or fast-track with a smart tool like the EngineerNow.org Resume Builder, you’ll create a professional, recruiter-ready resume that actually opens doors to interviews.

Final Checklist Before You Apply:

✔ Tailor the summary and skills to the specific job posting

✔ Lead with metrics and your biggest achievements

✔ Keep it ATS-friendly and easy to scan

✔ Remove outdated or irrelevant experience

✔ Proofread for clarity, grammar, and tone

Do I need to detail my education in resume?

It depends on experience level. Mid- or senior-level engineers can usually just list their degree, university, graduation year. Just starting out? Go all-in: show relevant coursework, capstone projects, and any hackathons or academic projects that prove your skills and experience.

Should I include projects if I don’t have commercial experience?

Yes—definitely. Listing public projects, such as GitHub repositories with GCP pipelines, in a data engineer resume can boost your chances of getting noticed by up to 30%.

Which certifications matter most for a GCP Data Engineer?

The “Google Cloud Professional Data Engineer” is the heavy hitter—it shows up in 92% of U.S. job postings. Other GCP certs are a plus; AWS or Azure certs can go at the end. Keep it recent and relevant—older certifications carry less weight.

What file format should I use to submit my resume?

Stick with PDF unless the job posting says otherwise (Word or RTF). ATS systems love clean, standard formatting, so don’t make your resume fight to get noticed.

How long and detailed should my resume be?

Keep it tight—1 page is ideal. Got a lot of achievements? 2 pages is fine, but only if every line shows impact. Recruiters skim fast, so make sure every bullet counts.