Rawaatib

Rawaatib (رواتب) is a community driven salary benchmarking platform built specifically to bring transparency to the Gulf region’s job market. It replaces workplace guesswork with verified, localized data so you can navigate your career and hiring decisions with absolute confidence.

A real-time salary intelligence and compensation registry built specifically for the Gulf Cooperation Council (GCC) region. Functioning as a crowd-sourced database, the platform provides verified market benchmarks to bring transparency to the tax-free job markets of the UAE, Saudi Arabia, Qatar, Kuwait, Bahrain, and Oman.

Services
Offered

Web Development

  • Web & App Development
  • Mathematical Modeling and Algorithms
  • SEO / SEM Development

Creative & Branding

  • Branding
  • Social Media Management
Core
Project
Features
  • Market Benchmarking: Tracks detailed salary percentiles (25th, Median, 75th) for over 200 mapped positions across 12 major industry sectors.
  • Comprehensive Analytics: Breaks down total compensation packages, factoring in 0% personal income tax alongside standard regional benefits like housing allowances, health insurance, and end-of-service gratuities.
  • Data Privacy: Aggregates peer-submitted payroll data with 100% anonymity, ensuring accurate insights with zero employer tracking.
Core
Project
Features
  • Verified Benchmarking: Users can search highly specific, crowd-sourced compensation data filtered by exact roles, industries, and GCC locations.
  • Market Percentile Tracking: Instead of relying on easily skewed averages, the platform displays the exact 25th percentile (low end), median (true market rate), and 75th percentile (high end) for every position.
  • Total Compensation Breakdowns: Provides granular visibility into regional pay structures, separating base salary from allowances like housing and transportation.
  • 100% Anonymity: A privacy-first architecture ensures that individual user submissions remain strictly confidential while collectively empowering the market.
The
Data Engine
& Algorithms

To handle large datasets securely and prevent market distortions, Rawaatib utilizes a robust backend system powered by advanced mathematical models:

Data Cleaning (The Interquartile Range / Tukey’s Fences): An anomaly detection algorithm that establishes strict statistical boundaries based on the middle 50% of submissions. It automatically flags and removes extreme outliers (like artificially inflated or troll submissions) before they enter the database.
True Market Ranges (Quantile Estimation / Linear Interpolation): This interpolates the cleaned data to pinpoint exact percentile markers rather than raw averages, ensuring the true middle of the market is accurately represented.

Historical Stability (Fixed-Weight Indexing / Laspeyres Index):

A macroeconomic model used to map historical salary trends. It holds the “basket” of job roles constant over time, ensuring that the sudden addition of new, high-paying executive titles does not artificially spike the platform’s overall market growth charts.

Data Normalization (Compound Annual Growth Rate / CAGR Adjustment):

A financial indexing algorithm that acts as a “salary ager.” It applies an annualized growth factor to historical submissions, adjusting older data points for inflation and cost-of-labor increases so they accurately reflect present-day value without dragging down current medians.

Next Project

Multinational School Dhahran