Check Your Resume Before You Apply

Most employers use software (an ATS) to read and rank resumes. See your score and fix it. Free, no signup to check.

Starrez
Senior Quality Engineer
Hyderabad, Telengana, IN

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How it works

Key Takeaways

  • Automated hiring systems can screen or route resumes before human review; ResumeGeni treats ATS scoring as parser-readiness triage, not a hiring prediction (Harvard Business School & Accenture).
  • The most common ATS-readiness problems are missing keywords, incompatible formatting, incomplete fields, and incorrect file types
  • ResumeGeni scores parseability, structure, contact fields, content completeness, skills, and keyword signals, then explains the evidence behind the score

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 use that data in search, review, and matching workflows. Parsing gaps can make a qualified candidate harder to evaluate.

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 signalsJob-specific terms, skills, certificationsKeyword overlap can affect recruiter search visibility and resume-review workflows
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 extraction means important fields may need manual review

What ResumeGeni Checks Before Keyword Matching

Keyword matching only helps after the resume can be read cleanly. ResumeGeni starts with parser-readiness signals before it evaluates wording, skills, and role fit.

  • Readable text: whether the uploaded file exposes selectable text instead of only a scanned image.
  • Standard resume structure: whether contact, summary, work experience, education, and skills sections are easy to identify.
  • Field extraction: whether names, email addresses, phone numbers, employers, titles, dates, degrees, and skills can be mapped into stable fields.
  • Format risk: whether tables, columns, text boxes, decorative icons, headers, footers, or unusual bullets could interrupt parsing.
  • Evidence quality: whether experience bullets include scope, tools, metrics, and outcomes rather than generic duty lists.
  • Keyword coverage: whether relevant tools, certifications, industry terms, and role-specific phrases appear naturally in the resume.

What Your ATS Score Means

The score is a diagnostic signal, not a hiring guarantee. A high score means ResumeGeni can extract and evaluate the resume with fewer warnings. A low score means the resume likely needs structural fixes before keyword tuning matters.

Score RangeReadBest Next Action
90-100Strong parser readiness with few visible gapsTailor keywords and achievements to the target role
75-89Generally readable, but some sections or evidence may be weakFix warnings, add measurable achievements, and tighten skills
60-74Important content may be missing, vague, or hard to mapRepair structure before rewriting bullets
Below 60Parsing or completeness issues are likely holding the resume backMove to a cleaner format and rebuild core sections first

What To Fix First

Start with problems that prevent a system or recruiter from reading the resume. Save small wording changes for after the structure is clean.

PriorityFixReason
1Use a text-based PDF, DOCX, or plain text resumeImage-only files and corrupted exports cannot be reliably parsed
2Use one column and standard headingsPredictable structure improves section and field detection
3Put contact information in the body, not only the headerSome parsers ignore header and footer regions
4Replace vague duties with quantified achievementsSpecific outcomes help both recruiter review and scoring evidence
5Mirror role language truthfullyRelevant keywords help search and review without keyword stuffing

How To Use the Score Without Overfitting

The best use of an ATS score is triage. Fix problems that make the resume hard to parse or hard to evaluate, then stop when the document is clear. Do not chase a perfect score by adding keywords you cannot defend in an interview or by turning every bullet into a list of tools.

Checker signalGood correctionCorrection to avoid
Low parse confidenceMove to a single-column layout, standard headings, and selectable text.Adding more keywords before the resume can be read cleanly.
Weak evidence bulletsRewrite duties into scope, action, tool, and measurable outcome.Inflating impact numbers or copying sample bullets that do not match your work.
Missing role termsAdd truthful tools, certifications, patient loads, stack details, or workflows from your experience.Keyword stuffing a skills section with technologies you have not used.
Thin company fitCompare the resume with the target role and company application guide before applying.Submitting the same generic version to every employer.

Methodology And Limits

ResumeGeni checks format, extraction, content completeness, and keyword signals from the uploaded resume. It does not certify that every employer ATS will parse the file the same way, and it does not predict whether a recruiter will interview you.

For the scoring rubric, privacy notes, and limitations, read the ATS Resume Checker Methodology. For the broader source map behind ResumeGeni guidance, use the research hub and dated research data dashboard. For application context, use the exact company application guide or role guide that matches the job.

What the Checker Can Diagnose

Treat the ATS resume checker as a document-readiness diagnostic, not a hiring prediction. A useful check should tell you whether the resume text can be extracted, whether the major sections are recognizable, whether contact fields are present, whether bullets contain evidence, and whether role language appears naturally enough for a reviewer to understand the match.

Diagnostic areaWhat ResumeGeni looks forBest correction
Text extractionSelectable text, readable file structure, and parser confidence.Use a text-based PDF, DOCX, or pasted text version before changing wording.
Section recognitionStandard headings for contact, summary, experience, education, skills, projects, and certifications.Rename creative headings to conventional resume sections and keep content in the document body.
Evidence qualityBullets with scope, action, tools, and measurable outcomes rather than generic duties.Rewrite the most recent role first, then work backward through older experience.
Role alignmentTruthful keywords, credentials, systems, technologies, and responsibilities that match the target role.Compare the resume with a role guide and a real posting before adding or removing terms.

Pair the Score With a Role Guide

An ATS score is the starting point. After the resume is readable, compare it with the role you are targeting so your skills, bullets, and keywords match the actual posting without keyword stuffing.

Resume pathUse this guide when the checker flagsBest next page
ClinicalMissing license, certification, patient-load, unit, EHR, or care-outcome evidenceRN resume guide
TechnicalThin stack detail, unclear shipped features, missing testing, deployment, or performance evidenceFull-stack developer resume guide or Android developer resume guide
PortfolioCase studies, client scope, shipped work, project outcomes, or collaboration signals are too vagueProduct designer resume guide or Freelancer resume guide
People operationsHRIS, compliance, hiring, retention, employee-relations, or policy examples are missingHuman resources manager resume guide

Where This Checker Fits in the Application Path

Use the checker as a diagnostic gate between drafting and applying. It is strongest when the next action is specific: fix parsing risks, rewrite vague bullets, add missing role evidence, or compare the resume against a real posting. It is weaker when treated as a hiring predictor or a substitute for role judgment.

Signal from the checkerBest next pageReason
Formatting or parsing warningsATS compatibility methodologyReview the scoring categories and limits before changing the file structure.
Weak or generic experience bulletsResume guides by job titleFind role-specific examples and replace duties with evidence, scope, and outcomes.
Missing tools, systems, or certificationsSkills guides by job titleCheck which skills belong in the resume and which should appear only when truthful.
Company-specific application concernsCompany application guidesCompare employer context, ATS signals, and open-role language before final tailoring.

Sources Used For This Checker

ResumeGeni's checker combines product analysis with public resume-writing, occupational, and structured-data references. These sources inform parser-readiness guidance; they do not certify that any employer or ATS vendor will score a resume the same way.

Frequently Asked Questions

Is ResumeGeni free?
Yes. 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 parsed into structured fields such as contact information, experience, education, and skills. The score reflects how cleanly ResumeGeni can extract those fields plus format, content, and keyword signals.
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.

Preferred ATS Checker Resource Spine

Built by ResumeGeni. Methodology, sources, and limitations are documented above. Last updated .

Senior Quality Engineer

Starrez · Hyderabad, Telengana, IN

About StarRez


StarRez is the global leader in student housing software, providing innovative solutions for on and off-campus housing management, resident wellness and experience, and revenue generation. Trusted by 1,400+ clients across 25+ countries, StarRez supports more than 4 million beds annually with its user-friendly, all-in-one platform, delivering seamless experiences for students and administrators. With offices in the United States, Australia, the UK, and India, StarRez blends the robust capabilities of a global organization with the personalized care and service of a trusted partner.

The Role 

As a Senior Quality Engineer - Data, you will play a pivotal role in elevating our quality practices across data products, analytics workflows, and complex distributed systems. You will be an expert within our engineering organization, deeply understanding product behaviour, source-to-consumption data flows, analytics use cases, and the systems that transform operational data into trusted customer and business insight. This role will balance hands-on individual contributor responsibilities with leading, influencing, and coaching your peers.

This senior individual contributor role is for someone who is passionate about seeing systems holistically, has a strong vision for data quality, and is eager to drive the highest standards of accuracy, completeness, reliability, performance, and analytical trust. You will leverage your deep understanding of data platforms, data pipelines, databases, reporting layers, and complex system integrations to guide and influence our development process.

Your role is not to manage a team, but to be a quality champion who partners directly with developers, data engineers, analytics engineers, product managers, and analysts. You will help teams anticipate risk, validate business logic, protect data integrity, and build more robust, scalable, and reliable data-enabled products. At the core of this role is a strong focus on the customer experience; success means our platform produces accurate, explainable, timely, and useful data that helps customers and internal teams make confident decisions.

What You Will Own 

  • Own and drive end-to-end testing strategy for complex distributed systems, data pipelines, analytics workflows, and reporting experiences, proactively identifying risks, integration challenges, data quality gaps, and performance bottlenecks while establishing a clear quality vision across the product.
  • Define and champion data quality standards for accuracy, completeness, consistency, freshness, uniqueness, validity, lineage, reconciliation, and analytical correctness across operational systems, data stores, transformations, APIs, and dashboards.
  • Design and execute validation strategies for source-to-target data flows, including ingestion, transformation, business rules, aggregations, joins, schema changes, data contracts, exports, and downstream analytics consumption.
  • Partner with Product, Design, Engineering, Data, and Analytics teams from ideation through delivery to ensure metrics definitions, acceptance criteria, test data, edge cases, and quality gates reflect the real business meaning of the data.
  • Provide quality-focused input on testability, scalability, observability, and reliability during ticket grooming and feature discussions, offering insights on product resilience, data integrity, usability, and analytics trust.
  • Build pragmatic automated checks and test frameworks for database validation, API validation, data reconciliation, pipeline regression testing, and analytics verification using common data and quality tools.
  • Design and execute integration testing and performance testing for distributed systems and data-heavy workflows, including validation of batch, streaming, event-driven, and near-real-time data processing where applicable.
  • Champion shift-left quality culture by mentoring developers, data engineers, and analytics partners on testing principles, data contracts, clear acceptance criteria, early validation, fast feedback loops, and production-quality observability.
  • Maintain data-driven quality management by implementing meaningful metrics and KPIs to assess data health, pipeline reliability, test coverage, defect trends, system maturity, and measurable impact of quality initiatives.
  • Partner with teams to identify systemic quality challenges such as inconsistent metric definitions, missing lineage, brittle transformations, weak test data, flaky pipeline checks, data drift, or gaps between product behaviour and analytics reporting.
  • Drive innovative quality engineering approaches for data and analytics products, taking accountability for successful execution and adoption across product development teams.

Required Qualifications 

  • 5+ years of progressive software engineering, quality engineering, data engineering, or analytics engineering experience, with deep focus on quality in complex, distributed system environments.
  • Proven track record as a senior individual contributor with strong quality mindset and ability to influence technical direction through innovative, pragmatic, and evidence-based approaches.
  • Deep hands-on experience working with data, including database validation, data profiling, data reconciliation, test data management, source-to-target testing, and validation of complex data flows.
  • Strong SQL skills and practical understanding of relational and non-relational data stores, data modelling concepts, schema design, indexing, query behaviour, and common data failure modes.
  • Experience validating analytics outputs such as dashboards, reports, operational metrics, customer-facing insights, semantic layers, or business KPIs, with ability to challenge definitions and trace results back to source data.
  • Familiarity with modern data and analytics tooling such as Snowflake, BigQuery, Redshift, Databricks, Spark, Airflow, Kafka, Tableau, Power BI, Great Expectations, Soda, Monte Carlo, or similar tools.
  • Strong automation or scripting skills, such as Python, JavaScript/TypeScript, Java, SQL, Bash, or PowerShell, with hands-on experience building testing infrastructure, automation frameworks, data checks, test harnesses, and quality utilities from the ground up.
  • Practical experience integrating automated tests and quality checks into CI/CD pipelines using tools such as GitHub Actions, GitLab CI, Azure DevOps, Jenkins, CircleCI, Buildkite, or similar platforms.
  • Experience defining deployment and release quality gates, including test suite selection, environment readiness checks, data migration validation, pipeline health signals, quality thresholds, reporting artifacts, and approaches for managing flaky or unreliable tests.
  • Exceptional systems-thinking mindset toward quality challenges, with natural ability to see integration complexities, performance implications, reliability concerns, data dependencies, and downstream analytics impact across the product.
  • Outstanding problem-solving skills with talent for root cause analysis of complex system and data behaviours, including issues caused by transformation logic, timing, orchestration, schema drift, stale data, duplication, or inconsistent business rules.
  • Deep experience designing performance, scalability, and reliability testing strategies for data-heavy products, including large data volumes, long-running pipelines, concurrent usage, API throughput, and reporting latency.
  • Excellent technical communication and collaboration skills to effectively guide and influence engineering peers, data partners, product managers, analysts, and business stakeholders through deep technical expertise and strategic quality insights.
  • Genuine curiosity and passion for understanding product architecture, user experience flows, customer workflows, analytics use cases, and the business impact of engineering and data decisions

Reasons to join our Team

  • Opportunity to be a part of a well-established, high-performance company that has been in business for over 30+ years
  • Full benefits including health care, paid time off, and others
  • A supportive team environment with emphasis on learning and development opportunities
  • Our Promise: You will learn, grow, and be appreciated for your impact and contributions.
  • Z-Factor: Our most celebrated value, you will work with a team of caring, high-performing, and passionate people who have fun supporting our vision, innovation, and continuous improvement.

Even if you don't have all of the Qualifications listed above, but feel you have what it takes to succeed in the role, we would love to hear from you! 

 

StarRez is an equal opportunity employer.