Check Your Resume Before You Apply

Most employers use software (an ATS) to read and rank resumes. See your score and fix it. Free, no signup to check.

Playbook
Senior Analytics Engineer
Europe (Remote)

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How it works

Key Takeaways

  • Automated hiring systems can screen or route resumes before human review; ResumeGeni treats ATS scoring as parser-readiness triage, not a hiring prediction (Harvard Business School & Accenture).
  • The most common ATS-readiness problems are missing keywords, incompatible formatting, incomplete fields, and incorrect file types
  • ResumeGeni scores parseability, structure, contact fields, content completeness, skills, and keyword signals, then explains the evidence behind the score

How ATS Resume Scoring Works

Applicant tracking systems parse your resume into structured data — extracting your name, contact info, work history, skills, and education — then use that data in search, review, and matching workflows. Parsing gaps can make a qualified candidate harder to evaluate.

LayerWhat It ChecksWhy It Matters
Document extractionFile format, encoding, readabilityCorrupted or image-only PDFs fail immediately
Layout analysisTables, columns, headers, footersMulti-column layouts break field extraction
Section detectionExperience, education, skills headingsNon-standard headings cause sections to be missed
Field mappingName, email, phone, dates, titlesMissing contact info is a common cause of immediate rejection
Keyword signalsJob-specific terms, skills, certificationsKeyword overlap can affect recruiter search visibility and resume-review workflows
Chronology checkDate ordering, gap detectionReverse-chronological order is expected by most ATS
QuantificationMetrics, numbers, measurable outcomesQuantified achievements help human reviewers and some scoring models
Confidence scoringOverall parse quality and completenessLow-confidence extraction means important fields may need manual review

What ResumeGeni Checks Before Keyword Matching

Keyword matching only helps after the resume can be read cleanly. ResumeGeni starts with parser-readiness signals before it evaluates wording, skills, and role fit.

  • Readable text: whether the uploaded file exposes selectable text instead of only a scanned image.
  • Standard resume structure: whether contact, summary, work experience, education, and skills sections are easy to identify.
  • Field extraction: whether names, email addresses, phone numbers, employers, titles, dates, degrees, and skills can be mapped into stable fields.
  • Format risk: whether tables, columns, text boxes, decorative icons, headers, footers, or unusual bullets could interrupt parsing.
  • Evidence quality: whether experience bullets include scope, tools, metrics, and outcomes rather than generic duty lists.
  • Keyword coverage: whether relevant tools, certifications, industry terms, and role-specific phrases appear naturally in the resume.

What Your ATS Score Means

The score is a diagnostic signal, not a hiring guarantee. A high score means ResumeGeni can extract and evaluate the resume with fewer warnings. A low score means the resume likely needs structural fixes before keyword tuning matters.

Score RangeReadBest Next Action
90-100Strong parser readiness with few visible gapsTailor keywords and achievements to the target role
75-89Generally readable, but some sections or evidence may be weakFix warnings, add measurable achievements, and tighten skills
60-74Important content may be missing, vague, or hard to mapRepair structure before rewriting bullets
Below 60Parsing or completeness issues are likely holding the resume backMove to a cleaner format and rebuild core sections first

What To Fix First

Start with problems that prevent a system or recruiter from reading the resume. Save small wording changes for after the structure is clean.

PriorityFixReason
1Use a text-based PDF, DOCX, or plain text resumeImage-only files and corrupted exports cannot be reliably parsed
2Use one column and standard headingsPredictable structure improves section and field detection
3Put contact information in the body, not only the headerSome parsers ignore header and footer regions
4Replace vague duties with quantified achievementsSpecific outcomes help both recruiter review and scoring evidence
5Mirror role language truthfullyRelevant keywords help search and review without keyword stuffing

How To Use the Score Without Overfitting

The best use of an ATS score is triage. Fix problems that make the resume hard to parse or hard to evaluate, then stop when the document is clear. Do not chase a perfect score by adding keywords you cannot defend in an interview or by turning every bullet into a list of tools.

Checker signalGood correctionCorrection to avoid
Low parse confidenceMove to a single-column layout, standard headings, and selectable text.Adding more keywords before the resume can be read cleanly.
Weak evidence bulletsRewrite duties into scope, action, tool, and measurable outcome.Inflating impact numbers or copying sample bullets that do not match your work.
Missing role termsAdd truthful tools, certifications, patient loads, stack details, or workflows from your experience.Keyword stuffing a skills section with technologies you have not used.
Thin company fitCompare the resume with the target role and company application guide before applying.Submitting the same generic version to every employer.

Methodology And Limits

ResumeGeni checks format, extraction, content completeness, and keyword signals from the uploaded resume. It does not certify that every employer ATS will parse the file the same way, and it does not predict whether a recruiter will interview you.

For the scoring rubric, privacy notes, and limitations, read the ATS Resume Checker Methodology. For the broader source map behind ResumeGeni guidance, use the research hub and dated research data dashboard. For application context, use the exact company application guide or role guide that matches the job.

What the Checker Can Diagnose

Treat the ATS resume checker as a document-readiness diagnostic, not a hiring prediction. A useful check should tell you whether the resume text can be extracted, whether the major sections are recognizable, whether contact fields are present, whether bullets contain evidence, and whether role language appears naturally enough for a reviewer to understand the match.

Diagnostic areaWhat ResumeGeni looks forBest correction
Text extractionSelectable text, readable file structure, and parser confidence.Use a text-based PDF, DOCX, or pasted text version before changing wording.
Section recognitionStandard headings for contact, summary, experience, education, skills, projects, and certifications.Rename creative headings to conventional resume sections and keep content in the document body.
Evidence qualityBullets with scope, action, tools, and measurable outcomes rather than generic duties.Rewrite the most recent role first, then work backward through older experience.
Role alignmentTruthful keywords, credentials, systems, technologies, and responsibilities that match the target role.Compare the resume with a role guide and a real posting before adding or removing terms.

Pair the Score With a Role Guide

An ATS score is the starting point. After the resume is readable, compare it with the role you are targeting so your skills, bullets, and keywords match the actual posting without keyword stuffing.

Resume pathUse this guide when the checker flagsBest next page
ClinicalMissing license, certification, patient-load, unit, EHR, or care-outcome evidenceRN resume guide
TechnicalThin stack detail, unclear shipped features, missing testing, deployment, or performance evidenceFull-stack developer resume guide or Android developer resume guide
PortfolioCase studies, client scope, shipped work, project outcomes, or collaboration signals are too vagueProduct designer resume guide or Freelancer resume guide
People operationsHRIS, compliance, hiring, retention, employee-relations, or policy examples are missingHuman resources manager resume guide

Where This Checker Fits in the Application Path

Use the checker as a diagnostic gate between drafting and applying. It is strongest when the next action is specific: fix parsing risks, rewrite vague bullets, add missing role evidence, or compare the resume against a real posting. It is weaker when treated as a hiring predictor or a substitute for role judgment.

Signal from the checkerBest next pageReason
Formatting or parsing warningsATS compatibility methodologyReview the scoring categories and limits before changing the file structure.
Weak or generic experience bulletsResume guides by job titleFind role-specific examples and replace duties with evidence, scope, and outcomes.
Missing tools, systems, or certificationsSkills guides by job titleCheck which skills belong in the resume and which should appear only when truthful.
Company-specific application concernsCompany application guidesCompare employer context, ATS signals, and open-role language before final tailoring.

Sources Used For This Checker

ResumeGeni's checker combines product analysis with public resume-writing, occupational, and structured-data references. These sources inform parser-readiness guidance; they do not certify that any employer or ATS vendor will score a resume the same way.

Frequently Asked Questions

Is ResumeGeni free?
Yes. ATS analysis, scoring, and initial improvement suggestions are free with no signup required. Full guidance and saved reports may require a free account.
What file formats are supported?
PDF, DOCX, DOC, TXT, RTF, ODT, and Apple Pages. PDF and DOCX are recommended for best ATS compatibility.
How is the ATS score calculated?
Your resume is parsed into structured fields such as contact information, experience, education, and skills. The score reflects how cleanly ResumeGeni can extract those fields plus format, content, and keyword signals.
Can ATS read PDF resumes?
Yes, but not all PDFs are equal. Text-based PDFs parse well. Image-only PDFs (scanned documents) and PDFs with complex tables or multi-column layouts often fail ATS parsing. Our analyzer will flag these issues.
How do I improve my ATS score?
Focus on three areas: use a clean single-column format, include keywords from the job description naturally in your experience bullets, and ensure all sections (contact, experience, education, skills) use standard headings.

Preferred ATS Checker Resource Spine

Built by ResumeGeni. Methodology, sources, and limitations are documented above. Last updated .

Senior Analytics Engineer

Playbook · Europe (Remote)

About Us

We are Playbook, a leading creator platform for fitness, health, and wellness. Our mission is to help fitness creators build sustainable businesses, while enabling hundreds of thousands of users to live healthier lives.

We are a fast-growing company in the fitness tech space, operating as a remote-first, product-driven team that values ownership, direct communication, and a strong growth mindset. We believe in "drivers, not passengers" — everyone is encouraged to take responsibility, think proactively, and act like an owner.

Role Overview

We're hiring our first dedicated data hire — a Senior Analytics Engineer with strong analytics chops who will own Playbook's data stack end-to-end.

Today, our data lands in BigQuery via Hevo syncs that replicate our production systems 1:1 — raw, unstructured, and waiting to be turned into something useful. Your mission is to design and build Playbook's BigQuery data warehouse from scratch leveraging an ELT approach through Dataform or dbt, establish the conventions, testing, and CI/CD that will scale with us, and make that warehouse directly serve the business — starting with our Growth team.

Beyond the warehouse, you'll be the go-to person for anything data-related across the company. You'll partner with our Growth team as their primary data counterpart — translating their questions into production-grade data models, codifying business metrics, and making data something the team can trust and move on.

This is a high-ownership, high-autonomy role reporting to the Head of Growth. You'll be the person the company turns to for anything data-related — from "how do we define MRR?" to "how do we attribute this signup to the right campaign?" to "why don't these two numbers match?". You'll set the bar for how data is built, trusted, and used at Playbook.


Responsibilities

  • Design and build Playbook's data warehouse from the ground up in Dataform or dbt on BigQuery — defining our raw/staging/intermediate/marts architecture, modeling conventions, naming, and testing standards.

  • Own our ingestion layer — manage and extend our Hevo setup across Stripe, production Postgres (AWS), Mixpanel, GA4, HubSpot, Meta Ads, Google Ads, Ahrefs, PostHog, and new sources as they come.

  • Establish CI/CD, testing, and data quality practices for the warehouse — environments, automated tests, lineage, freshness checks, and alerting so we can trust what we ship.

  • Be the Growth team's data partner — turn their questions into production-grade data models, define and codify business metrics (MRR, churn, LTV, CAC, activation, retention, attribution), and make self-serve analytics actually self-serve.

  • Own, build, and evolve Playbook's creator-facing analytics product — the data layer that powers the metrics and insights creators see inside the platform about their own business performance.

  • Support product and engineering teams on data-heavy features — partner on data models, pipelines, and metric definitions for features that rely on the warehouse.

  • Own data requests across the company — triage, prioritize, and either solve them directly or invest in the models that unblock them at scale.

  • Maintain and evolve our BI layer — making sure dashboards and reports are trustworthy, documented, and built on top of our modeled layer rather than raw tables.

  • Set the direction for Playbook's data platform — what to build vs. buy, where to invest, and how the stack should evolve as we grow.


Requirements

  • 5+ years of experience in data engineering, analytics engineering, or a hybrid role — with a track record of owning a data warehouse in a production environment.

  • Expert-level SQL and deep experience with BigQuery (or a comparable cloud warehouse: Snowflake, Redshift, Databricks).

  • Hands-on experience with Dataform or dbt — building modular, tested, documented ELT pipelines and enforcing conventions across a codebase.

  • Strong grasp of dimensional modeling — facts, dimensions, slowly changing dimensions, incremental models, and knowing when to denormalize vs. normalize.

  • Fluent with CI/CD for data — Git workflows, environment separation (dev/staging/prod), automated tests, and deployment pipelines for warehouse code.

  • Experience with a managed ingestion tool like Hevo (what we use today) or similar — and a solid intuition for when these tools are enough vs. when to build custom.

  • Hands-on experience with Metabase — including an understanding of how its capabilities and quirks shape warehouse design decisions. Familiarity with other BI tools (Tableau, Power BI, AWS QuickSight) is a plus.

  • Product-minded engineering — you can design data that is consumed by end users in an application, not just by internal dashboards. You think about performance, API shape, and data contracts.

  • Experience working with LLMs / AI in data workflows — using AI to accelerate modeling, documentation, SQL generation, or building natural-language interfaces on top of the warehouse.

  • Excellent communication in English — you can explain technical trade-offs to non-technical stakeholders and partner with Growth, Product, and Engineering on equal footing.

  • Ownership mindset — comfortable being the first data person, making decisions with incomplete information, and being accountable for outcomes, not just tickets.

Nice to Have

  • Prior experience in an Data Engineering or BI Engineering role — sitting at the intersection of data engineering and business.

  • Strong understanding of SaaS and subscription business models — you're fluent in MRR, ARR, churn, deferred revenue, LTV, and CAC, and you know where the tricky edge cases live (refunds, coupons, trials, annual plans, revenue recognition).

  • Experience at a creator economy, marketplace, or subscription platform.

  • Experience building or integrating experimentation / A/B testing infrastructure — exposure assignment, metric computation, stats pipelines.

  • Experience with Python for ad-hoc data work, custom ingestion scripts, or orchestration.

What We Offer

  • Your work will directly affect creators and users on the platform. You'll work on features that ship quickly and matter.

  • We offer a wide compensation range to reflect different seniority profiles within this role. The upper end of the range is reserved for top candidates who demonstrate exceptional technical quality, product thinking, and ownership beyond day-to-day execution.

  • Equity options.

  • 100% remote with flexible working hours and async-friendly culture. Collaboration across Europe and the US East Coast.

  • A collaborative team that values ownership, open communication, and autonomy over micromanagement.

  • Yearly team retreats focused on connection, alignment, and building strong team relationships.

  • Paid Time Off.