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 Engineer

Nimblegravity · LATAM (Remote)

At Nimble Gravity we are looking for a talented Data Engineer to join our team.

 

Primary Responsibilities   

  • Build, scale, and maintain robust data solutions to support the firm's objectives.
  • Implement and optimize high-performance data pipelines: extraction, loading, transformation, and orchestration – that are designed for scalability, reliability, maintainability, and speed.
  • Lead software development projects end to end involving large language models (LLMs), retrieval-augmented generation (RAG) frameworks, and other AI technologies.
  • Champion modern software engineering practices as CI/CD, infrastructure-as-code, containerization, and cloud-native deployments
  • Collaborate closely with business stakeholders to transform use cases into production-ready services and solutions, owning the system from concept to production.
  • Implement rigorous testing and monitoring practices to maintain superior data quality and integrity.
  • Mentor and develop junior team members, fostering a culture of excellence and continuous learning within the team.
  • Be willing to travel up to 20% of the time to collaborate with distributed team members across locations.  

 

Requirements  

Education & Certificates 

  • A bachelor's degree or higher in a STEM field, required
  • Concentration in Computer Science, Math, Physics or other engineering related field, preferred 

 

Professional Experience 

  • 5+ years of experience in data engineering or a related discipline, with a proven track record of success. 
  • Experience in the financial services or private equity industry, preferred  

 

Competencies & Attributes 

  • Expertise in Python and SQL, with a strong foundation in data manipulation and analysis.
  • Proficient with Databricks/PySpark and dbt for data warehousing and data transformation tasks.
  • Experience with workflow orchestration tools e.g. Airflow, Temporal
  • Experience working with large language models (LLMs) especially prompt engineering, retrieval-augmented generation (RAG)s, and/or vector databases.
  • Knowledge of fundamental principles of machine learning, feature engineering, and knowledge graphs are pluses.
  • Demonstrated experience in designing and implementing complex data systems from the ground up.
  • Proficient in handling large-scale data projects, including data cleaning, ETL, and information retrieval.
  • Previous experience in a product development or financial services environment is highly desirable.
  • Excellent communication skills required, both verbal and written. 

 

 

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