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 .

Staff Data Engineer

Arine · Remote (United States of America)

Based in San Francisco, Arine is a rapidly growing healthcare technology and clinical services company with a mission to ensure individuals receive the safest and most effective treatments for their unique and evolving healthcare needs. 

Frequently, medications cause more harm than good. Incorrect drugs and doses costs the US healthcare system over $528 billion in waste, avoidable harm, and hospitalizations each year. Arine is redefining what excellent healthcare looks like by solving these issues through our software platform (SaaS). We combine cutting edge data science, machine learning, AI, and deep clinical expertise to introduce a patient-centric view to medication management, and develop and deliver personalized care plans on a massive scale for patients and their care teams.

Arine is committed to improving the lives and health of complex patients that have an outsized impact on healthcare costs and have traditionally been difficult to identify and address. These patients face numerous challenges including complicated prescribing issues across multiple medications and providers, medication challenges with many chronic diseases, and patient issues with access to care. Backed by leading healthcare investors and collaborating with top healthcare organizations and providers, we deliver recommendations and facilitate clinical interventions that lead to significant, measurable health improvements for patients and cost savings for customers. 

Why is Arine a Great Place to Work?:

Outstanding Team and Culture - Our shared mission unites and motivates us to do our best work. We have a relentless passion and commitment to the innovation required to be the market leader in medication intelligence.

Making a Proven Difference in Healthcare - We are saving patient lives, and enabling individuals to experience improved health outcomes, including significant reductions in hospitalizations and cost of care.

Market Opportunity - Arine is backed by leading healthcare investors and was founded to tackle one of the largest healthcare problems today. Non-optimized medications therapies which cost the US 275,000 lives and $528 billion annually.

Dramatic Growth - Arine is managing more than 18 million lives across prominent health plans after only 4 years in the market, and was ranked 236 on the 2024 Inc. 5000 list and was named the 5th fastest-growing company in the AI category.

The Role:

As a key technical leader and team architect working in a fast-paced environment, you will drive the design, development, and optimization of scalable data ingestion pipelines within the Arine platform. Leveraging expert-level proficiency in Python and AWS, you will architect solutions that handle diverse file types and large-scale healthcare datasets. You will have a direct impact on building reusable, configurable tools set for handling data needs for the entire company.

What You'll be Doing:

  • Act as the team architect by leading system design reviews, offering recommendations, conducting comprehensive peer reviews, and demonstrating expert-level proficiency in Python and AWS services

  • Architect and implement scalable data ingestion pipelines that handle different file types into the Arine platform

  • Develop reusable components that integrate into data pipelines to increase efficiency and reduce future implementation time

  • Create configuration-driven, containerized toolsets that are easy to use and maintain across diverse engineering profiles

  • Work collaboratively with cross-functional teams to meet data requirements through ETL components

  • Design and maintain data transformation pipelines using DBT, including macros, incremental models, and DBT tests

  • Implement incremental data ingestion strategies for large-scale healthcare datasets

  • Build monitoring and alerting systems for data ingestion processes and overall pipeline health

  • Apply software engineering best practices, including test-driven development and modular design, to data infrastructure

  • Refactor and rebuild existing data ingestion processes to improve scalability and operational efficiency

  • Work with containerization technologies (Docker, Kubernetes) to create portable and maintainable data solutions

  • Identify and escalate inefficiencies within and across teams

  • Provide technical guidance and mentorship to junior engineers, and promote best practices and coding standards

  • Author and maintain high-quality technical documentation, and support junior engineers in doing the same

  • Collaborate with the DE Manager to report on DE contractor performance issues.

Who You Are and What You Bring:

  • 10+ years working in data engineering, with a focus on large-scale data ingestion and infrastructure

  • Deep expertise in Python and modern data engineering tools

  • A track record of building automated, production-grade ETL processes using Python and dbt SQL

  • Strong understanding of ETL/ELT frameworks and distributed data processing

  • Hands-on proficiency with modern data technologies and comfort leveraging AI coding assistants to accelerate development, improve code quality, and enhance productivity

  • Skilled in data processing, validation, cleaning, and debugging

  • Strong capability integrating APIs for seamless data exchange between systems

  • Proven ability to handle and process varied file types and formats, including healthcare standards such as HL7, 834, 837, and NCPDP

  • Demonstrated success integrating and consolidating data from diverse source systems into a unified repository, including EHR and claims systems, via both file-based and API integrations

  • Comfort working with large-scale datasets (10GB+)

  • Strong capability implementing incremental processing and change data capture (CDC) methodologies

  • Extensive background designing scalable data architectures in AWS environments

  • Solid grounding in software engineering principles, including test-driven development, loose coupling, single responsibility, and modular design

  • Hands-on familiarity with containerization (Docker, Kubernetes) and building configuration-driven, maintainable systems

  • Proven ability to build tools and systems that diverse engineering profiles can operate through configuration rather than code changes

  • A passion for building new data infrastructure and continuously improving existing systems with robustness, maintainability, and operational excellence

  • Familiarity with healthcare data and regulatory environments (HIPAA) as a plus

  • Strong collaboration skills, with comfort partnering across technical and non-technical stakeholders

  • Excellent written and verbal communication, with the ability to explain technical infrastructure concepts to diverse audiences

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