What 2,027 Job Descriptions Reveal About Hiring in 2026

Most resume advice starts with a generic list: communication, teamwork, problem-solving, leadership.

That is not what the job descriptions say.

We analyzed 2,027 recent public job postings across 10 roles — from software engineering and data to product, project management, marketing and UX — and counted the skills, tools and methods that actually appear in the text. The result is not a ranking of what matters most. It is a map of what employers mention, role by role.

Here is what stood out.

The short version

• The most common term in each role is usually specific: roadmap for product managers, SQL for data analysts, machine learning for data scientists, prototyping for UX designers.
• Communication or stakeholder management still appears in the top five for seven of the ten roles — but usually alongside a role-specific cluster, not instead of one.
• Technical roles split into two conversations: tools (Python, Kubernetes, Figma) and production ownership (testing, security, observability, scalability).
• The Software Engineer sample is the broadest and largest, and no single term appears in even half of those postings. A 48% top term is not weak data; it reflects how many different kinds of software jobs share one title.

The data at a glance

The percentages mean: the share of postings in that role that mention the term at least once. They do not measure how important the term is, and they are not a universal benchmark.

What the roles actually emphasize

Product, project and business roles are about decisions

The three roles closest to planning and delivery share a vocabulary:

• Product Manager: roadmap (82.5%), prioritization (65.8%), stakeholder management (64.6%).
• Project Manager: stakeholder management and communication (76.2% each), cross-functional work (70.7%), team leadership (65.2%).
• Business Analyst: stakeholder management (88.3%), requirements gathering (79.2%), communication (67.5%).

The common thread is not “communication” as a soft skill. It is the ability to turn an ambiguous request into a priority, a requirement or a delivery plan — and keep several teams aligned while doing it.

A resume that says “excellent communication skills” does not show any of that. A resume that says “ran requirements workshops with sales and support, then cut scope to ship the billing migration on time” does.

The data roles are not interchangeable

Data Analyst, Data Scientist and Data Engineer are often treated as one family. The job descriptions are not:

• Data Analyst: SQL (72.9%), reporting (67.8%), stakeholder management (66.1%), dashboards (60.2%). The output is a decision-ready view of the business.
• Data Scientist: machine learning (79.0%), Python (73.1%), statistics (67.2%), communication (65.5%), SQL (56.3%). The output is a model, an experiment or a causal answer.
• Data Engineer: Python (66.7%), SQL (55.9%), requirements (55.9%), data modeling (47.7%), data pipelines (46.8%). The output is reliable data infrastructure.

If you are targeting one of these roles, do not copy a resume from another. The nouns change, and so does the evidence a hiring manager expects.

Engineering and DevOps split into tools and production ownership

For Software Engineers, the most-mentioned terms are scalability (48.3%), testing (42.7%), security (42.6%) and observability (39.3%). Python is next at 36.6%. That is a reminder that “which language?” is only one question; the posting also asks whether you can operate what you build.

For DevOps Engineers, the list is more concrete: networking (65.4%), Kubernetes (59.0%), security (57.1%), Python (51.3%), AWS (49.4%). The job is a mix of infrastructure, automation and incident response — not just a list of cloud logos.

Marketing and UX balance craft with cross-functional work

• Marketing Manager: communication (74.3%), cross-functional collaboration (64.4%), campaign management (63.4%), go-to-market (55.4%), product marketing (55.4%).
• UX Designer: prototyping (80.0%), portfolio (70.8%), cross-functional collaboration (55.4%), design systems (50.8%), Figma (41.5%).

Both roles need a portfolio of evidence. For marketing, the evidence is a campaign, budget or pipeline result. For UX, it is a case study: the problem, the research, the trade-off and the shipped outcome.

What this does not tell you

A keyword analysis has limits, and they matter:

• Mentions are not requirements. A term can appear once in a “nice to have” list or five times in the core responsibilities. We counted presence, not weight.
• Synonyms are missed. A posting may ask for “customer success” while a resume says “account management.” The data will not connect them.
• The sample skews toward tech and remote-first companies. Greenhouse and Ashby make up most of the postings. Traditional enterprise, healthcare, government and non-English roles are underrepresented.
• The sample is not seniority-weighted. A junior and a staff posting can share the same title. The keyword percentages do not tell you what changes with level.
• Software Engineer dominates the total. At 853 of 2,027 postings, it is 42% of the dataset. That is why we report role-level percentages instead of one blended “most in-demand skill” number.

Treat this as a map for reading a job description, not a replacement for reading the job description.

How to use this in practice

1. Start with the role, not the industry. A Data Analyst and a Data Scientist resume should not lead with the same keywords. Use the role page for your target.
2. Read the posting's must-haves first. The dataset shows patterns; the specific posting shows priorities.
3. Put the term inside evidence. “SQL” is a skill. “Built 12 SQL reports for a 40-person sales team” is a reason to believe it.
4. Check the gaps against a real job description. A generic score is less useful than a match against the posting in front of you.
5. Do not chase 100%. A strong match is roughly 70–85%, backed by examples you can explain in an interview. For the reasoning, see what a good ATS score actually means.

Explore your role

• Data Analyst
• Software Engineer
• Business Analyst
• Product Manager
• Project Manager
• Data Engineer
• Data Scientist
• DevOps Engineer
• Marketing Manager
• UX Designer

Each role page shows the full keyword map, the source mix and the sample size — not just the top three terms.

Methodology

We collected public postings from Greenhouse, Ashby, Lever, Arbeitnow, RemoteOK, Jobicy, Working Nomads and Remotive between October 19 and October 26, 2026. The final sample is 2,027 postings across 10 role families. The largest source is Greenhouse (1,399), followed by Ashby (406), Arbeitnow (115), RemoteOK (52), Lever (27), Jobicy (22), Working Nomads (5) and Remotive (1).

For each role, we counted whether a curated list of skills, tools, methods and responsibilities appeared at least once in the posting text. Percentages are the share of postings in that role that mention the term. The lists and role families are maintained in the Jobalytics role data and are updated as new postings arrive.

This is a descriptive analysis, not a controlled survey. It is useful for spotting language patterns; it is not a representative sample of every job market, industry or seniority level.

Check your resume against the posting in front of you

The most useful next step is not to memorise a list. It is to compare your resume with the job you are applying for.

Check your resume free →

If you want to extract the terms from a posting first, use the job description keyword finder. If you want the AI-assisted explanation of the gaps, see Jobalytics AI.