IT · Software Engineer / Data · 8 min read · based on 500 postings
What software and data job postings actually require
A practical map of skills that recur in software engineering and data roles — and how to score your resume against a real posting instead of guessing.
IT hiring postings are noisy: long wish lists, optional tools framed as requirements, and years-of-experience thresholds that rarely map 1:1 to screen outcomes. Looking at what recurs across many Software Engineer and data-oriented postings is a better starting point than copying a generic “top 10 skills” list.
Requirements that show up most (software)
Share of software postings mentioning each requirement
- A mainstream backend language (e.g. Python, Java, Go)92%
- Cloud experience (AWS, GCP, or Azure)74%
- REST / API design68%
- SQL and relational databases61%
- CI/CD and automated testing54%
- Containers (Docker / Kubernetes)43%
Data / analytics stack that recurs
Share of data / analyst-style postings
- SQL89%
- A BI tool (Tableau, Power BI, or Looker)71%
- Spreadsheet modeling (Excel / Sheets)63%
- Python or R for analysis48%
What matters less than the wishlist suggests
- A specific university brand — often preferred, rarely a hard gate.
- Every framework in the list — depth in one stack beats shallow breadth.
- Exact years thresholds — frequently listed, unevenly enforced.
Simulated HireForge run — one SWE resume vs three postings
We scored a simulated mid-level backend engineer (Python/Go, AWS, APIs, CI/CD; light Kubernetes) against three sample IT postings using HireForge’s factor rubric.
Simulated resume — Mid Software Engineer
6 years backend (Python/Go), AWS + Postgres, REST APIs, CI/CD. Limited Kubernetes ownership; no public portfolio of distributed systems at scale.
Illustrative simulation using HireForge’s scoring rubric — not a live API run and not a guarantee of outcomes.
Hiring score
Backend Engineer
Stripe-like payments SaaS
Factor breakdown
- Qualification Match78%
- Screening Odds72%
- Competitive Edge58%
- Company & Role Fit74%
- Credential Hurdles88%
- Timeline Feasibility80%
Solid baseline fit; container orchestration and reliability narratives are the main gaps.
Strengths
- Strong overlap on Python/Go, APIs, and cloud
- CI/CD evidence matches screening keywords
Gaps / risks
- Kubernetes depth is thin vs posting emphasis
- Fewer scale/reliability stories than peer bar
Hiring score
Software Engineer, Data Platform
Shopify-scale commerce
Factor breakdown
- Qualification Match64%
- Screening Odds61%
- Competitive Edge52%
- Company & Role Fit60%
- Credential Hurdles85%
- Timeline Feasibility70%
Adjacent role: core engineering helps, but platform-specific stack gaps pull the score down.
Strengths
- SQL and backend fundamentals transfer
- AWS experience is relevant
Gaps / risks
- Data-platform tooling (Spark/Airflow) under-evidenced
- Batch/streaming ownership not clear on resume
Hiring score
Senior Software Engineer
Observability / Datadog-like
Factor breakdown
- Qualification Match55%
- Screening Odds48%
- Competitive Edge42%
- Company & Role Fit50%
- Credential Hurdles80%
- Timeline Feasibility55%
Title seniority mismatch: closing competitive-edge and qualification gaps matters before applying.
Strengths
- Solid backend craft
- Cloud + testing hygiene present
Gaps / risks
- Senior bar expects deeper ownership and mentorship signals
- Distributed systems evidence below posting ask
Hiring score across three IT postings
- Stripe-like payments SaaS: Backend Engineer73%
- Shopify-scale commerce: Software Engineer, Data Platform62%
- Observability / Datadog-like: Senior Software Engineer52%
How to use this
Map your resume to the high-frequency requirements for the role you want, then score against a concrete posting. The gaps that hurt most are the common ones you cannot evidence yet — that is what a HireForge factor breakdown surfaces.