Key Takeaways

  • 75% of U.S. employers use automated applicant tracking systems to screen resumes before a human reviews them (Harvard Business School & Accenture, 2021)
  • The most common ATS failures are missing keywords, incompatible formatting, and incorrect file types
  • ResumeGeni scores your resume across 8 parsing layers — modeled on the same steps enterprise ATS platforms like Workday, Greenhouse, and Taleo use to evaluate candidates

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 score how well that data matches the job requirements. Many ATS rejections happen because the parser couldn't extract critical fields, not because the candidate wasn't qualified.

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 matchingJob-specific terms, skills, certificationsKeyword overlap affects recruiter search visibility and ATS scoring
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 parses get deprioritized in results

Frequently Asked Questions

Is ResumeGeni free?
Yes. ResumeGeni is currently in beta — 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 processed through an 8-layer parsing pipeline that extracts structured data the same way enterprise ATS platforms do. The score reflects how completely and accurately your resume can be parsed, plus how well your content matches common ATS ranking criteria.
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.

ATS Guides & Resources

Built by engineers with 12 years of experience building enterprise hiring technology at ZipRecruiter. Last updated .

Data Analyts

Aliancers · United States

Data Analyst

📍LATAM - 100% Remote

🤝Contract - USD

🗣️Advanced English skills is required

We are looking for 3 Data Analysts, for 3 different teams: Product & Growth; Operations & Finance; and Experimentation & Digital Journey.

Role Summary

Seek versatile Data Analysts specialized in Data Quality. These roles ensure reliable, actionable insights across product analytics, financial reconciliation, and experiment measurement by embedding measurement quality into instrumentation, data modeling, testing, and reporting. The candidates will own end-to-end data integrity, governance, and cross-functional collaboration to drive data-driven decisions.

Key Responsibilities

  • Data Modeling & Reconciliation (Cross-Functional): Build and maintain semantic data models linking operational sources (ticketing, retail, inventory, marketing) to finance systems and product metrics. Engineer reconciliation processes comparing operational datasets to gold sources (general ledger, ERP) and define acceptable variances with automated outlier alerts.
  • Instrumentation QA & Data Quality Automation: Audit and validate analytics instrumentation (web/app) end-to-end: data layer → Tag Manager → GA4 (or Mixpanel/Amplitude) → data warehouse → BI. Validate event firing, privacy settings, and campaign tagging (UTMs, pixels). Develop automated data quality tests (freshness, completeness, uniqueness, reconciliation) using SQL (Snowflake, BigQuery, Redshift), Python, and frameworks like Great Expectations; schedule via Airflow/Snowflake Tasks and alert on failures.
  • Dashboarding & Reporting: Create and maintain dashboards in Power BI, Looker, Sigma, or Tableau that reflect accurate metrics and QA indicators (pass/fail badges). Ensure traceability of metrics and readiness for executive and stakeholder reviews.
  • Data Contracts, Governance & Documentation: Define data contracts for critical datasets (schema, nullability, validity thresholds) and implement checks to enforce contracts. Document data models, QA processes, SOPs, and runbooks; manage task/incident logs in Jira or ServiceNow; collaborate with product, engineering, marketing, finance, and operations in an Agile environment.
  • Cross-Functional Collaboration & Training: Partner with product, marketing, finance, and IT to plan measurement, drive data quality improvements, and educate business users on data governance and best practices.
  • Data Quality Assurance in Diverse Domains: Support quality across Product & Growth metrics, revenue attribution and tagging for finance, and experiment measurement pipelines; contribute to continuous improvement of data collection, storage, and processing.

Core Technical Skills

  • SQL & Python: Proficient SQL for querying/testing data in cloud data warehouses (Snowflake, BigQuery, Redshift). Python for data validation scripts and ETL/test automation; familiarity with Pandas and testing frameworks (PyTest, Great Expectations).
  • Analytics Tools: GA4 (custom events, dataLayer), Google Tag Manager (GTM) or similar for web/app tagging, and ad conversion pixels (Google Ads, Meta, TikTok, CM360). Experience with tag validation tools (GA Debugger, Tag Assistant).
  • BI & Visualization: Experience building dashboards in Power BI, Sigma, Tableau or Looker. Ability to translate raw data into clear visual reports. Familiarity with DAX/Power Query (for Power BI) or SQL-based modeling (for Sigma).
  • Programming: Proficiency SQL skills for large-scale reconciliations (Snowflake or similar). Comfortable with Python for data validation scripts and ETL jobs. Experience with libraries like Pandas for data processing and PyTest or Great Expectations for automated tests. Basic JavaScript skills for debugging tags via browser DevTools (Console/Network).
  • Web & Analytics Instrumentation: Deep familiarity with GA4, Google Tag Manager (GTM) or similar, custom events, dataLayer, and campaign tagging; basic JavaScript for debugging and tag validation.
  • Data Quality & Observability: Exposure to dbt for transformation/testing and data observability platforms (e.g., Monte Carlo) is a plus. Understanding CI/CD for data assets (Git, GitHub Actions, Jenkins).
  • Collaboration & Process: Experience with Jira or ServiceNow for issue tracking and Confluence (or internal wiki) for documentation. Strong communication skills to work with technical and non-technical stakeholders.
  • Scripting & QA: JavaScript and DevTools for debugging, with SQL/Python to automate data quality checks.

Originally posted on Himalayas