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 .

Director, Data Engineering

Everway · Remote- UK

At Everway, our goal is to lead the world in Neurotechnology software, helping transform the way we understand and are understood. 

We’re a global community of over 600 team members spanning seven countries, including the UK, USA, Norway, Denmark, Sweden, Australia, and New Zealand. By understanding and addressing the unique needs of each individual, we're creating a world where differences are recognized and valued. A world where everyone can thrive.

We can only achieve our goals and continue to grow by having high performing people in our team, people who share our goals and are passionate about our mission. We pride ourselves on our core values that are embedded within our culture. These are to be curious, have courage, and commit fully.

Join us at Everway - together, we can unlock the full potential of every mind.

About the role

Everway is a growing EdTech SaaS business formed through multiple acquisitions, resulting in a complex, multi-product data landscape. We’re building a modern, scalable data platform, and this role is central to establishing the engineering foundations that make trusted, high-quality data products possible.

As Engineering Lead, you will own the data platform and standards that enable consistent, reliable data across the business. You’ll lead a team of data engineers while remaining hands-on in delivery, partnering closely with Data Architecture, Analytics, and Governance to ensure data is built for quality, scalability, and consumption.

Main responsibilities

  • Lead, mentor, and develop a team of data engineers, fostering a culture of ownership, quality, and collaboration
  • Contribute hands-on to the design and build of data pipelines, integrations, and platform components
  • Own and evolve the Databricks-based data lakehouse (Delta Lake, Unity Catalog), including architecture, performance, and lifecycle management
  • Define and enforce engineering standards across ingestion, transformation (dbt), naming conventions, access controls, and environment management
  • Design scalable ingestion patterns (e.g., Fivetran) to support multiple source systems, including M&A-driven complexity
  • Ensure reliable, well-documented ingestion with full history preservation and monitoring
  • Partner with Data & Analytics on data contracts and modelling to ensure data is fit for downstream use cases
  • Embed data quality, lineage, and governance into engineering workflows
  • Drive engineering best practices across code quality, testing, CI/CD, documentation, and observability
  • Own and optimise the transformation layer (dbt), including structure, testing, and performance
  • Support operational excellence, including incident response and SLA adherence
  • Partner with leadership on hiring, team growth, and capacity planning

Essential Criteria

  • 3+ years in data engineering, with 2+ years in a leadership or senior technical role
  • Experience operating in complex environments (e.g., M&A, multi-system landscapes, platform migrations)
  • Strong hands-on experience with Databricks (Delta Lake, Unity Catalog, Spark)
  • Proficiency in Python and SQL for building data pipelines
  • Experience with dbt or equivalent transformation frameworks
  • Experience building and maintaining scalable data pipelines (e.g., Fivetran, Airflow, or similar)
  • Strong understanding of data modelling, warehousing concepts, and lakehouse architecture
  • Proven ability to define and enforce engineering standards (testing, CI/CD, documentation, observability)
  • Experience with cloud platforms (preferably AWS)
  • Ability to balance hands-on technical work with team leadership and stakeholder collaboration
  • Strong communication skills, with the ability to translate technical decisions into business impact

Desirable Criteria

  • Experience working in a data product operating model with defined data contracts and SLAs
  • Familiarity with data quality and observability tools (e.g., dbt tests, Great Expectations, Monte Carlo)
  • Background in SaaS environments, including CRM (Salesforce) or ERP data
  • Experience with infrastructure-as-code or DevOps practices (e.g., Terraform, Databricks Asset Bundles)
  • Exposure to semantic layer tools (e.g., MetricFlow)
  • Experience supporting or leading M&A data integration or platform migrations
  • Familiarity with BI tools (e.g., Tableau, Power BI) and how data is consumed downstream
  • Experience working in agile environments and managing engineering backlogs

 

Please submit your application on out website by Wednesday 22nd April 2026.

 

Please note: applications may close early due to high demand, so early submission is encouraged.

Join our team and enjoy a competitive salary with bonus opportunities, flexible work schedules, and comprehensive health and wellness benefits. We offer flexible time off plans, career growth through development programs, and a collaborative, innovative culture where your ideas matter.  Ready to make an impact? Apply today and be part of a company that invests in your success!

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