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 .

2024_MS_EDE3_XC_SRE_DataEngineering

Bosch · bangalore, IN

Information Technology Associate REF239880G



As a Site Reliability Engineer (SRE), you will be responsible for ensuring the reliability, scalability, and performance of the systems necessary for the product and services for the Data Engineering Projects.  

You will work closely with function developers, Architects and DevOps teams to build and maintain high-availability systems, capable of handling high workloads automate with active monitoring of the infrastructure.  

As SRE you would ensure system reliability, availability for continuous deployment as part of the Agile practices in solution development. Mandatory Skills & experience in: 

Experience with cloud platforms specifically Azure. 

Hands on experience and proficiency in Cloud infrastructure and CI/CD frameworks for providing IaC -  Terraform, ARM, YAML and cloud native containerization & deployment of Services viz. Docker, k8s, etc 

Hands-on experience with large scale Azure DevOps and Azure PaaS components. 

Must have tool knowledge – Argo, Terraform (CLI), Azure-CLI, KubeCtl, Flux, Helm, Argo (Events and workflows), Istio, Grafana, Kustomize, YAML based coding and debugging skills 

Must have Kubernetes admin skill set, good to have knowledge about tools/extension to Kubernetes 

Experience in understanding of function development of data science solutions & programming languages e.g. Python, Go  

Excellent problem-solving skills and attention to detail. 

Hands-on experience with architecting and development of features using u-Service application principles  

Deep understanding of Service Level Objectives (SLOs), Service Level Indicators (SLIs), error budgeting and configuring KPIs for highly sophisticated services. 

Experience with the ELK stack (Elasticsearch, Logstash, Kibana) and Prometheus for monitoring and logging. 

Solid expertise in applying cloud security best practices through DevSecOps principles, with a deep understanding of Kubernetes (k8s) security. Preferred Skills & experience in: 

Experience with DevOps, data pipelines and various messaging systems on a Cloud native setup (MS Azure) 

Experience with database technologies (MongoDB, NoSQL, etc.) and cloud native optimization services 

Strong working knowledge in Azure 

Motivating attitude, profound communication, strong interpersonal skills, structured and analytical 

Knowledge of costing, optimization techniques for large scale cloud native services. Key Responsibilities: 

System Reliability: Design and engineer highly scalable and high availability systems for high throughput workloads. 

Continuous monitoring & active alerting: Develop, deploy, and manage monitoring systems, setting up alerts to proactively identify and resolve issues. 

Automation: Automate routine tasks such as deployments, monitoring, and policy enforcements using suitable frameworks  

Performance Tuning: Optimize system performance by identifying bottlenecks and implementing appropriate solutions. 

Infrastructure as Code (IaC): Utilize tools like Terraform, Ansible, or similar to manage infrastructure through code, ensuring consistency and repeatability. 

Security: Understand the implement the security policy and enforcements defined by the organization for infrastructure and data 

Scaling & Cost Management: Analyze system performance and plan for future scaling needs. 

Issue Handling and resolution: Respond to system outages, perform root cause analysis, and implement fixes to prevent future incidents.  

Master's degree/ Bachelor Degree in Computer Science or Information Science or equivalent engineering stream.

6-8 Years of hands on experience in maintaining Large scale, High availability Data engineering solutions, services.