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 Modeler - Azure Databricks (Medallion Architecture)

Rackspace · United Arab Emirates - Dubai

Rackspace Technology is a leading provider of expertise and managed services across all the major public and private cloud technologies. We’ve evolved Fanatical Support to encompass the entire customer journey — providing Fanatical Experience™ from first consultation to daily operations. Our passionate experts combine the power of proactive, always-on service and expertise with best-in-class tools and automation to deliver technology when and how our customers need it. As a Data Modeler, you will design and deliver implementation-ready logical and physical data models within Azure Databricks (Delta Lake) following Medallion Architecture (Bronze, Silver, Gold). The role ensures Data Engineers can immediately begin development with clear schema definitions, SQL structures, and zero ambiguity. Key Responsibilities Data Modelling & Design • Develop Conceptual, Logical, and Physical Data Models. • Define schemas, entities, attributes, relationships, keys, constraints, and data types. • Provide DDL-ready SQL definitions for implementation. • Clearly define fact table grain and dimensional structures. Azure Databricks – Medallion Modelling • Design structured models across: o Bronze Layer – Raw ingestion, schema control. o Silver Layer – Cleansed, conformed, standardized data. o Gold Layer – Business-ready dimensional models. • Define transformation logic across layers (Bronze → Silver → Gold). • Incorporate Delta Lake best practices (partitioning, performance, scalability). Advanced Modelling Techniques. Requires strong hands-on expertise in: • 3rd Normal Form (3NF) • Star Schema • Snowflake Schema • Data Vault (Hubs, Links, Satellites) Must clearly define: • Fact grain • Surrogate key strategy • Slowly Changing Dimensions (Type 1 & 2) • Conformed dimensions • Aggregation strategy Engineering & Governance Alignment • Ensure models are build-ready for Data Engineering teams. • Review implementation alignment with design. • Maintain ER diagrams, data dictionary, mapping documents. • Enforce enterprise naming standards and modelling best practices. Required Skills & Experience • 5+ years of hands-on Data Modelling experience. • Strong SQL (DDL, partitioning, performance concepts). • Proven experience in Azure Databricks and Delta Lake environments. • Deep understanding of Medallion Architecture. • Experience in enterprise Data Warehouse or Modern Data Platform projects. • Proficiency with data modelling tools (ERwin, ER Studio, • PowerDesigner, Lucidchart, or similar).