This dataset provides insights into data engineer salaries and employment attributes for the year 2024. It includes information such as salary, job title, experience level, employment type, employee residence, remote work ratio, company location, and company size.

The dataset allows for analysis of salary trends, employment patterns, and geographic variations in data engineering roles. It can be used by researchers, analysts, and organizations to understand the evolving landscape of data engineering employment and compensation.

Feature Description:

• work_year: The year in which the data was collected (2024).
• experience_level: The experience level of the employee, categorized as SE (Senior Engineer), MI (Mid-Level Engineer), or EL (Entry-Level Engineer).
• employment_type: The type of employment, such as full-time (FT), part-time (PT), contract (C), or freelance (F).
• job_title: The title or role of the employee within the company, for example, AI Engineer.
• salary: The salary of the employee in the local currency (e.g., 202,730 USD).
• salary_currency: The currency in which the salary is denominated (e.g., USD).
• salary_in_usd: The salary converted to US dollars for standardization purposes.
• employee_residence: The country of residence of the employee.
• remote_ratio: The ratio indicating the extent of remote work allowed in the position (0 for no remote work, 1 for fully remote).
• company_location: The location of the company where the employee is employed.
• company_size: The size of the company, often categorized by the number of employees (S for small, M for medium, L for large).

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Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Engineers, all other occupations: 16 years and over: Women (LEU0254747000A) from 2000 to 2024 about engineering, second quartile, occupation, full-time, females, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

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Content:

The 2024 dataset on data developer salaries and employment attributes offers valuable insights into the evolving landscape of data developers. It includes key variables such as salary, job title, experience level, employment type, employee residence, remote work ratio, company location, and company size. This data enables detailed analysis of salary trends, employment patterns, and geographic variations in data developer roles. Researchers, analysts, and organizations can leverage this dataset to better understand compensation trends, the distribution of data developer roles across different regions, and the impact of remote work and company size on employment in this field.

Feature Description:

experience_level: Level of professional experience (e.g., junior, mid, senior).

employment_type: Type of job contract (e.g., full-time, part-time, contract).

job_title:The specific role or title of the employee (e.g., Data Engineer).

salary: The compensation received, in the original currency.

salary_currency:The currency in which the salary is paid.

salary_in_usd:The salary converted into US dollars for comparison.

employee_residence: The location where the employee resides.

remote_ratio: Percentage of work done remotely.

company_location: The geographical location of the company.

company_size: The scale of the company, often based on employee count.

Dataset Strecture:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12598114%2Fa8367f6e89faff096c3d370734462554%2Fsalary%20dataset.png?generation=1718089330150530&alt=media" alt="">

This dataset contains a list of data engineering job postings scraped from Glassdoor in the USA (March 2023). It includes details such as the company name, location, job title, job description, estimated salary, company size, company type, company sector, company industry, the year the company was founded, and company revenue. The dataset can be used for exploring data engineering job trends in the USA, analyzing salaries, and identifying the most in-demand skills and qualifications.

You can see the whole project on GitHub.

How to use

• Identify the education and experience required for jobs
• Find the top recruiting companies and the industry they work in
• Explore date engineering skills required in job descriptions
• Predict salary based on location, industry, company rating etc

Graph and download economic data for Employed full time: Wage and salary workers: Software developers, applications and systems software occupations: 16 years and over (LEU0254477200A) from 2000 to 2019 about software, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

H-1B visa sponsorship trends for Senior Data Engineer, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

In 2021, the average salary of an IT professional who held a Google Cloud Certified Professional Data Engineer certification stood at ******* U.S. dollars. The other Google certification Google Certified Professional Cloud Architect was the second top-paying certification, with an average salary of ******* U.S. dollars.

Graph and download economic data for Employed full time: Wage and salary workers: Petroleum engineers occupations: 16 years and over: Men (LEU0254586700A) from 2000 to 2024 about engineering, petroleum, occupation, full-time, males, salaries, workers, 16 years +, wages, employment, and USA.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Industrial engineers, including health and safety occupations: 16 years and over (LEU0254532700A) from 2000 to 2024 about safety, engineering, second quartile, occupation, full-time, health, salaries, workers, earnings, 16 years +, wages, median, employment, industry, and USA.

Description of the features in dataset:

• work_year: The year in which the salary data was collected (e.g., 2024).
• experience_level: The level of experience of the employee (e.g., MI for Mid-Level).
• employment_type: The type of employment (e.g., FT for Full-Time).
• job_title: The title of the job (e.g., Data Scientist).
• salary: The salary amount.
• salary_currency: The currency in which the salary is denominated (e.g., USD for US Dollars).
• salary_in_usd: The salary amount converted to US Dollars.
• employee_residence: The country of residence of the employee (e.g., AU for Australia).
• remote_ratio: The ratio indicating the level of remote work (0 for no remote work).
• company_location: The location of the company (e.g., AU for Australia).
• company_size: The size of the company (e.g., S for Small).

United States - Employed full time: Wage and salary workers: Architecture and engineering occupations: 16 years and over: Men was 2746.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Architecture and engineering occupations: 16 years and over: Men reached a record high of 2821.00000 in January of 2023 and a record low of 2073.00000 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Architecture and engineering occupations: 16 years and over: Men - last updated from the United States Federal Reserve on October of 2025.

Graph and download economic data for Employed full time: Wage and salary workers: Civil engineers occupations: 16 years and over (LEU0254478900A) from 2000 to 2024 about civil engineering, engineering, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

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United States - Employed: Paid at prevailing federal minimum wage: Wage and salary workers: Architecture and engineering occupations: 16 years and over was 0.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed: Paid at prevailing federal minimum wage: Wage and salary workers: Architecture and engineering occupations: 16 years and over reached a record high of 4.00000 in January of 2013 and a record low of 0.00000 in January of 2006. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed: Paid at prevailing federal minimum wage: Wage and salary workers: Architecture and engineering occupations: 16 years and over - last updated from the United States Federal Reserve on November of 2025.

United States - Employed full time: Wage and salary workers: Engineering technicians, except drafters occupations: 16 years and over: Men was 382.00000 Thous. of Persons in January of 2019, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Engineering technicians, except drafters occupations: 16 years and over: Men reached a record high of 426.00000 in January of 2000 and a record low of 253.00000 in January of 2014. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Engineering technicians, except drafters occupations: 16 years and over: Men - last updated from the United States Federal Reserve on November of 2025.

Backend engineering jobs of 43 cities in various states across the US. Aggregated .csv sheet and by city with and an additional data scientist job dataset in sampled cities like San Francisco, LA and New York.

Dataset Overview: The collection spans a variety of roles, from Assistant Civil Engineers to Construction Project Managers, detailed across several entries. Each record is a window into the diverse opportunities available, reflecting the sector's breadth and depth.

Data Science Applications: This dataset is primed for analytical explorations, from trend analysis and market demand assessments to skill gap identification. Its structured format is ideal for machine learning applications, such as predictive analytics for career path advancements or salary predictions, and NLP tasks like job description analysis or automated matching of candidates to job listings.

Column Descriptors: - company_name: The hiring entity's name. - job_title: The specific role or position offered. - job_description: A detailed account of the role, responsibilities, and qualifications. - job_posted_date: Date the job listing was published. - job_salary/currency, job_salary/currency_symbol, job_salary/estimated: Salary information including currency, symbol, and whether the salary is an estimate. - job_salary/pay_period: The salary distribution frequency (e.g., hourly, annually). - job_industry: The industry segment the job belongs to. - company_short_name: An abbreviated form of the company's name. - company_sizes_str: The size category of the company based on the number of employees. - company_foundation_date: The year the company was established. - company_revenue: The company's revenue range.

Ethically Mined Data: The dataset is constructed with ethical considerations at the forefront, ensuring the information is publicly available, non-invasive, and respectful of privacy norms. It adheres to data protection guidelines, offering insights without compromising individual or corporate confidentiality.

Acknowledgment: A nod to Glassdoor for its role in aggregating and making accessible such detailed job market data. This dataset leverages the platform's rich repository of job listings, providing a snapshot that's invaluable for analysis, insight generation, and decision-making in the job market.

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