Research · Updated 2026-10-25

ATS Resume Keyword Study: 3,518 Job Postings, Counted Term by Term

Published 2026-09-13 · Data collected 2026-08-29 to 2026-09-09 · Updated 2026-10-25 · By ZoeVera · Update history

Most resume keyword lists are somebody's opinion. This one is a count. Between 2026-08-29 and 2026-09-09 we read every open posting on 81 companies' public job boards for seven roles, 3,518 postings in all, and counted how many of them name each term.

Each row below links to that role's full table, where every term carries its own count and interval. The method and the limits are at the bottom of this page, and they apply to all seven.

The most common term in each role's job postings

Software engineer scalability52.9% of 1,360 postings
Account executive enterprise70.9% of 921 postings
Product manager roadmap85.5% of 421 postings
Marketing cross-functional59.9% of 347 postings
Cybersecurity Python53.3% of 212 postings
DevOps and SRE automation81.3% of 134 postings
Accountant audit85.4% of 123 postings

See which of these words your resume is missing

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Or read the resume keyword guides by role.

The seven role studies: samples and collection dates

Role	Postings	Companies	Terms	Collected
Software engineer →Practice words outrank the languages	1,360	72	45	2026-08-29
Account executive →A methodology is named more often than the CRM	921	52	43	2026-09-09
Product manager →The vocabulary PM resumes lead with is largely absent	421	62	44	2026-09-09
Marketing →The ad platforms barely appear; pipeline does	347	62	44	2026-08-30
Cybersecurity →Python is asked for more often than any certification	212	51	40	2026-08-29
DevOps and SRE →Named tools rank higher here than in any other role	134	41	42	2026-09-09
Accountant →The credential and the close, ahead of any system	123	49	30	2026-08-29

3,518 postings, 81 distinct companies. The role totals sum to more company entries than that, because many companies hire for several of these roles.

What job postings have in common across roles

The generic word beats the branded one. In every role where both were counted, the practice outranks the product that implements it: observability over Grafana, SIEM over Splunk, experimentation over A/B testing. A resume that names only the tool misses the word the posting actually uses.

Tools are not the top of any list except one. Software engineer and the rest lead with practices; DevOps is the single role where named products reach the top five, and even there automation at 81.3% leads them.

The same work is described in different words by role. That is the reason these are seven studies and not one list: the terms that matter for accountant postings barely occur in software engineer postings, and a single combined ranking would hide both.

Across roles

The same term in different roles

Most terms belong to one role. Of the 248 distinct terms counted across the seven studies, 28 appear in more than one, and 19 of those were matched with the identical pattern in every study that counted them. For those, a difference between two roles is a difference in the postings. They are listed below, widest spread first.

Term	Highest	Lowest	Other roles	Spread	Reading
Terraform	62.7% DevOps & SRE	13.2% Software Engineer	Cybersecurity 17%	49.5 points	Clear gap
Kubernetes	70.1% DevOps & SRE	23.1% Cybersecurity	Software Engineer 30.9%	47 points	Clear gap
forecasting	44.2% Account Executive	9.8% Accountant	None	34.4 points	Clear gap
Linux	38.8% DevOps & SRE	4.8% Software Engineer	Cybersecurity 11.8%	34 points	Clear gap
enterprise	70.9% Account Executive	40.1% Product Manager	None	30.8 points	Clear gap
SaaS	49.6% Account Executive	21.6% Product Manager	None	28 points	Clear gap
Python	70.9% DevOps & SRE	44.8% Software Engineer	Cybersecurity 53.3%	26.1 points	Clear gap
GCP / Google Cloud	43.3% DevOps & SRE	19.3% Software Engineer	Cybersecurity 34%	24 points	Clear gap
on-call	42.5% DevOps & SRE	18.9% Cybersecurity	Software Engineer 20.7%	23.6 points	Clear gap
funnel	22.8% Marketing	5.7% Product Manager	None	17.1 points	Clear gap
Bash / shell	20.9% DevOps & SRE	5.7% Cybersecurity	None	15.2 points	Clear gap
pipeline	65.5% Account Executive	52.2% Marketing	None	13.3 points	Clear gap
Azure	30.6% DevOps & SRE	17.6% Software Engineer	Cybersecurity 21.7%	13 points	Clear gap
Looker / Tableau	14.7% Marketing	3.3% Accountant	None	11.4 points	Clear gap
Salesforce	29.2% Account Executive	23.1% Marketing	None	6.1 points	Clear gap
HubSpot	6.6% Marketing	0.8% Account Executive	None	5.8 points	Clear gap
Rust	9.9% Software Engineer	5.2% DevOps & SRE	None	4.7 points	Within noise
CRM (generic)	18.2% Marketing	15% Account Executive	None	3.2 points	Within noise
Figma	2.4% Product Manager	2.3% Marketing	None	0.1 points	Within noise

Each share is out of that role's own sample, whose size is in the studies table above. A role is listed only if its study counted the term. That is not a claim that other postings never say it. “Clear gap” means the highest and lowest roles differ by more than sampling noise at 95%, tested as two independent samples.

The widest clear gap is Terraform: 62.7% of DevOps & SRE postings against 13.2% of Software Engineer postings, 49.5 points apart.

The narrowest spread among terms counted in three or more roles is Azure: 17.6% to 30.6% across the 3 roles that counted it.

16 of the 19 terms differ between their highest and lowest role by more than sampling noise. The other 3 are within it.

9 more terms appear in more than one study but are left out, because the studies defined them differently. The DevOps study counts “Amazon Web Services” under AWS, for example, and the others do not, so putting those percentages side by side would compare definitions, not roles.

Method & limits

Methodology

Every posting came from a public endpoint, the counting rules are stated below, and every figure on this page is in the downloadable CSV.

Data source. The Greenhouse job board API, which companies publish so their own careers pages can render their listings. The accountant study also reads the Lever postings API. Nothing scraped, nothing private. All postings were publicly accessible at the time of collection.

Sample selection. Title match on the role, as each study's page defines it. The software engineer, sales and DevOps/SRE studies exclude manager titles, because a posting for the person who runs the team asks for different things than a posting for someone on it. The product manager, marketing, cybersecurity and accountant studies filter by title alone, so manager-level and senior postings that match the title are in those samples.

Deduplication. One requisition advertised in several cities returns several records. Company, normalized title and content length together decide whether two records are the same posting. The rule is shared across all seven studies, so the sample sizes stay comparable and each posting is counted once.

Term counting. Document frequency: the share of postings that mention the term at least once, not how often it is repeated. Each role has its own term list, and terms outside that list were not counted. This measures whether a term appears, not how prominently.

Statistical margins. Confidence intervals follow the sample size. At 1,360 postings (software engineer) no figure moves more than about ±2.7 percentage points; at 123 (accountant) the worst case is about ±8.8 percentage points. Read close rows as ties rather than as a strict ranking.

Reproducibility. The study can be re-run against the same public endpoint. The collector downloads every open posting from the 81 companies' boards into one corpus. Each role's analyzer filters that corpus by title, deduplicates it and counts its terms: software engineer, account executive, product manager, marketing, cybersecurity, devops and sre, accountant. The scripts are the ones that built the published tables, served as plain text. Two analyzers were tightened after their collection, and the corrections log says which. Open roles change daily, so a re-run on a later date measures a different set of postings.

About this research

Research organization. This study is published by ResumeFitting, an independent resume optimization tool. We built this corpus because the resume keyword advice we found online was largely anecdotal: lists compiled from opinions rather than measured from actual job postings.

Research motivation. Job seekers are told to “include keywords” without being told which ones actually appear in the postings they're applying to. This study answers that question with data: for each role, here are the terms that postings actually use, measured at scale.

What this cannot tell you. These are public job boards from two ATS vendors (Greenhouse for six studies, Greenhouse and Lever for the accountant study), mostly US technology companies, mostly venture-funded. Banks, hospitals, government and agencies post elsewhere, and they ask for different things. Absence from this list is not evidence that a term doesn't matter; it only means it wasn't counted in this sample.

It is a snapshot, not a trend. Every study is one day of open roles, between 2026-08-29 and 2026-09-09. Comparing two of these studies across roles is fair; reading them as change over time is not.

And an aggregate is not your application. One posting weights its own terms differently from all 3,518. The study tells you which words the market uses; only the posting in front of you says which ones you need.

What a figure here means

A percentage is the share of the postings in a study’s sample that name the term at least once. It is not the share of all employers that ask for it, a cutoff an applicant tracking system applies, or a measure of your chances of an interview.

The 95% interval shows sampling error only. It does not correct for which companies publish on the job boards it draws from or for the one-day snapshot. A larger sample for one role says nothing about which role is more in demand or more important.

Questions about the study

What keywords appear most often in job postings?

It depends on the role, which is why the study is split seven ways. The most common term in each: Software engineer, scalability (52.9%); Account executive, enterprise (70.9%); Product manager, roadmap (85.5%); Marketing, cross-functional (59.9%); Cybersecurity, Python (53.3%); DevOps and SRE, automation (81.3%); Accountant, audit (85.4%).

How was the ATS resume keyword study conducted?

Every open posting was read from 81 companies' public Greenhouse job boards between 2026-08-29 and 2026-09-09, kept if its title matched one of seven roles, and deduplicated on company, normalized title and content length. That left 3,518 postings, each checked against its role's term list.

What does the percentage next to each term mean?

The share of that role's postings that name the term at least once. A posting that repeats a term ten times counts once, the same as one that mentions it in passing, so the figure measures how widely a term is asked for, not how heavily.

Can I use or cite this data?

Yes. The data is licensed CC BY 4.0: free to use, share and adapt, including commercially, with attribution to ResumeFitting and a link to this page so readers can see the sample and its limits. The full dataset is a CSV with one row per term, and each role study offers its own.

Download the dataset

Complete dataset: 288 terms across 3,518 postings

Every term from all seven studies, one row each. Includes role, term, category, share of postings, raw count, sample size, margin of error, and collection date. Machine-readable CSV format.

The 288 rows are one per role and term, so a term that two roles both counted (Python, for example) has two rows. They cover 248 distinct terms. The file has one header line above the 288 data rows.

Note: Margin of error is empty for software engineer studies, collected before per-term margins were recorded. Use the worst-case figures from the methodology section for those samples.

Download CSV

Licensed CC BY 4.0

How to cite this study

Quote any figure on this page. If you do, link back to this page so readers can see the sample and its limits rather than the number alone.

General citation

ResumeFitting, “ATS Resume Keyword Study: 3,518 Job Postings, Counted Term by Term” (method v1.0, data collected 2026-08-29–2026-09-09). https://resumefitting.com/ats-resume-keyword-study

APA style

ResumeFitting. (2026-09-13). ATS Resume Keyword Study: 3,518 Job Postings, Counted Term by Term. https://resumefitting.com/ats-resume-keyword-study

MLA style

ResumeFitting. “ATS Resume Keyword Study: 3,518 Job Postings, Counted Term by Term.” ResumeFitting, 2026-09-13, resumefitting.com/ats-resume-keyword-study.

Chicago style

ResumeFitting. “ATS Resume Keyword Study: 3,518 Job Postings, Counted Term by Term.” ResumeFitting, 2026-09-13. https://resumefitting.com/ats-resume-keyword-study.

Last collected: 2026-09-09
Method version: v1.0: document frequency, deduped on company, normalized title and content length
Corpus: 3,518 postings · 81 companies · 7 roles · 288 terms
Data: CSV above, one row per term with its count, share and margin
License: CC BY 4.0: use, share and adapt, including commercially, with attribution

Questions about the method, or want a term counted that is not in the list? support@zoevera.com.

Sources

There is one external source. Everything else on this page is computed from the postings it returned.

Greenhouse job board API is the public endpoint every posting was read from, on 81 companies' boards.
The seven role studies on this site, each carrying its own full table, term list and margins: software engineer, account executive, product manager, marketing, cybersecurity, devops and sre, accountant.

Update history

2026-09-13: this page was published, with the seven studies aggregated and the combined CSV released.
2026-09-09: collected account executive, product manager, devops and sre.
2026-08-30: collected marketing.
2026-08-29: collected software engineer, cybersecurity, accountant.

Each study is a single day's snapshot of open roles. When a role is re-collected the figures here, on its own page and in the CSV change together, and the new date is added to this list. Figures withdrawn or corrected after publication are listed in the corrections log.

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