Datasets:

datapizza-ai-lab
/

salaries

license: cc-by-nc-4.0
language:
  - it
tags:
  - salaries
  - italy
  - tech
  - survey
  - compensation
pretty_name: Datapizza Salaries
size_categories:
  - 10K<n<100K
dataset_info:
  features:
    - name: jobTitle
      dtype: string
    - name: monthsOfExperience
      dtype: int64
    - name: educationType
      dtype: string
    - name: companyIndustry
      dtype: string
    - name: companySize
      dtype: string
    - name: province
      dtype: string
    - name: workMode
      dtype: string
    - name: technologies
      sequence: string
    - name: salary
      dtype: int64
    - name: age
      dtype: int64
    - name: gender
      dtype: string
    - name: aiUsageFrequency
      dtype: int64
    - name: aiTechnologies
      sequence: string
    - name: aiTaskTypes
      sequence: string
    - name: aiUpdateSources
      sequence: string
    - name: admiredTechnologies
      sequence: string
    - name: editor
      dtype: string
    - name: rating
      dtype: int64
    - name: valid
      dtype: bool
    - name: submittedAt
      dtype: string

Datapizza Salaries

A crowd-sourced dataset of salaries from tech workers in Italy, collected via anonymous survey by Datapizza.

Dataset Description

This dataset contains self-reported salary and professional information from technology workers across Italy. Data is collected through an anonymous survey and updated weekly.

Note: Data submitted before November 6th, 2024 contains only partial information, as the initial survey version did not collect fields like educationType, age, aiUsageFrequency, and others.

Features

Field	Type	Description
`jobTitle`	string	Job title (e.g., "software_developer", "data_scientist")
`monthsOfExperience`	int	Professional experience in months
`educationType`	string	Education level (optional)
`companyIndustry`	string	Industry sector (optional)
`companySize`	string	Company headcount range (optional)
`province`	string	Italian province
`workMode`	string	Work arrangement
`technologies`	list[string]	Primary technologies/skills used
`salary`	int	RAL (Gross Annual Salary) in EUR
`age`	int	Respondent age (optional)
`gender`	string	Gender (optional)
`aiUsageFrequency`	int	AI usage frequency 1-5 (optional)
`aiTechnologies`	list[string]	AI tools used (optional)
`aiTaskTypes`	list[string]	AI task types (optional)
`aiUpdateSources`	list[string]	AI news sources (optional)
`admiredTechnologies`	list[string]	Technologies respondent wants to learn (optional)
`editor`	string	Primary code editor/IDE (optional)
`rating`	int	Survey feedback rating 1-5 (optional)
`valid`	bool	Submission validation flag
`submittedAt`	string	Submission timestamp (ISO 8601)

Categorical Values

educationType

high_school, bachelors_degree, masters_degree, phd

companySize

1-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5001+

workMode

onsite, hybrid, remote

gender

male, female, non_binary, prefer_not_to_say

companyIndustry

bancario, assicurativo, finanziario_altro, consulenza_servizi_professionali, tecnologia_ict, retail_gdo, moda_lusso, manifattura_generica, automotive, chimica_materiali, energia_utilities, telecomunicazioni, sanita_pharma_biotech, media_intrattenimento_editoria, logistica_trasporti, immobiliare_costruzioni, alimentare_beverage, agricoltura_agroindustria, turismo_ospitalita, istruzione_ricerca, non_profit_ong_associazioni, difesa_aerospazio, pubblica_amministrazione_enti_governativi, altro_indeterminato

aiTechnologies

chat_gpt, claude, cursor, claude_code, gemini, microsoft_copilot, github_copilot, codeium, tabnine, amazon_code_whisperer, jetbrains_ai, perplexity, mistral_ai, llama, v0, windsurf, cody, supermaven, continue_dev, devin, replit_ai, bolt

aiTaskTypes

coding, study_tutoring, doc_analysis, data_excel, editing, brainstorming, image_generation, translation, search_research, planning, meeting_notes

aiUpdateSources

x_twitter, newsletter, reddit, hugging_face, hacker_news, youtube, linkedin, podcasts, discord, github, official_blogs

editor

vscode, cursor, neovim, vim, zed, intellij_idea, webstorm, pycharm, sublime_text, emacs, atom, eclipse, visual_studio, android_studio, xcode, windsurf, rider, fleet, phpstorm, goland, rubymine, clion, notepad_plus_plus, helix

Usage

from datasets import load_dataset

dataset = load_dataset("datapizza-ai-lab/salaries")
df = dataset["train"].to_pandas()

# Filter for remote workers in Milan
remote_milan = df[(df["province"] == "Milano") & (df["workMode"] == "remote")]

# Average salary by job title
avg_by_role = df.groupby("jobTitle")["salary"].mean().sort_values(ascending=False)

Source

Data collected via salaries.datapizza.tech — an anonymous salary survey for Italian tech workers.

License

CC-BY-NC-4.0 — You may share and adapt this dataset with attribution for non-commercial purposes.

Citation

@dataset{datapizza_salaries_2024,
  title={Datapizza Salaries: Italian Tech Worker Compensation Dataset},
  author={Datapizza},
  year={2024},
  url={https://huggingface.co/datasets/datapizza-ai-lab/salaries},
  license={CC-BY-NC-4.0}
}