MENA Job Market Analytics Pipeline

A production-grade data pipeline that continuously collects and standardizes job postings across MENA countries, making it easy to answer questions like: Which industries are hiring the most? Who are the top hiring companies? Which technical skills, soft skills, certifications, and keywords are most in demand right now?

Objectives

🎯

Build a unified, analytics-ready view of the MENA job market.

🔍

Enable deep insights by country, industry, company, and skills.

📊

Power dashboards that track hiring trends and in-demand skills over time.

Pipeline Architecture

End-to-end data pipeline from web scraping through Bronze/Silver/Gold data lake to PostgreSQL warehouse with Redis caching and analytics dashboards.

Data Collection

High-volume scraping from LinkedIn

•Playwright + asyncio
•Country-specific scrapers
•Anti-bot protections

AWS S3 Data Lake

Bronze Layer

Store raw jobs exactly as scraped.

•Raw JSON in S3
•Partitioned data
•Run metadata

Silver Layer

Cleaned fields and AI-enriched jobs.

•Data cleansing
•OpenAI skills extraction
•Deduplication

Gold Layer

Normalized, analytics-ready datasets.

•Normalized schemas
•Parquet storage
•Pre-aggregated views

Warehouse & Cache

Dimensional warehouse with caching layer.

•Fact + dimension tables
•Redis cache layer
•Optimized for BI

Consumers

Dashboards and ad-hoc analysis.

•Country & industry trends
•Top companies & skills
•Future ML use-cases

Layer-by-Layer Deep Dive

Detailed breakdown of each layer's responsibilities, implementation details, and engineering patterns.

🕷️Web Scraping Layer

We built a high-performance scraping layer using Playwright and asyncio to continuously collect job postings from multiple portals and countries, while staying under anti-bot limits.

What happens here

•Continuously scrape job postings from LinkedIn, Bayt, NaukriGulf for multiple MENA countries.
•Capture job metadata and full descriptions in a structured format.
•Handle different site structures, filters and pagination per country.

Tech & tricks

•Python + Playwright + asyncio with a tuned browser pool.
•Anti-bot: rotating user agents, realistic headers, random delays, stealth mode.
•Resilient error handling and retries with backoff.
•Can reach steady jobs-per-minute on a modest server.

🗄️Bronze Layer – Raw Jobs Data

Store raw scraped jobs as JSON exactly as scraped, with minimal processing for traceability.

What happens here

•Store raw scraped jobs as JSON exactly as scraped.
•Partition by platform, country, and execution time.
•No heavy business logic, just 'as-is' ingestion.

Tech & tricks

•S3 as the raw data lake.
•Simple schema validation to catch broken files.
•Attach run metadata (site, country, timestamp) for traceability.

⚙️Silver Layer – Cleaned & Enriched

Clean, standardize, and AI-enrich job data with skills, industries, and metadata extraction.

What happens here

•Clean and standardize basic fields (titles, locations, salary ranges).
•Use AI to enrich jobs with skills, industries, seniority, etc.
•Remove duplicates across sites and countries.

Tech & tricks

•Python + Pandas + PyArrow for transforms.
•OpenAI models for skills/industry extraction with batched requests.
•Dedup engine combining hashing and fuzzy matching.
•Quality thresholds (e.g. minimum enrichment success %) before data can move forward.

💎Gold Layer – Analytics-Ready

Normalize jobs into consistent schemas and materialize curated datasets for fast analysis.

What happens here

•Normalize jobs into consistent schemas for analysis.
•Materialize curated views/tables for common queries.
•Export Parquet datasets for fast reads.

Tech & tricks

•Columnar Parquet storage in S3.
•Partitioning on date/country/industry.
•Pre-aggregated tables for 'top skills / industries / companies'.

🏛️Warehouse & Cache (PostgreSQL + Redis)

Dimensional warehouse with Redis caching layer for instant dashboard responses.

What happens here

•Load Gold data into a dimensional warehouse (facts + dimensions).
•Serve queries for dashboards and ad-hoc analysis.
•Cache hot queries for instant response.

Tech & tricks

•PostgreSQL with dim_job, dim_company, dim_location, dim_skill, dim_industry + fact_job_posting.
•Redis cache between Postgres and the application layer.
•Cache patterns for top industries, top companies, trending skills.

📊Consumers (Dashboards & Analytics)

Interactive dashboards and analytics tools for exploring job market trends and insights.

What happens here

•Dashboards to explore hiring trends by country, industry, company, skill.
•Ad-hoc SQL for deeper analysis.
•Future ML / recommendation workloads.

Tech & tricks

•React-based dashboard with ECharts visualizations.
•Direct PostgreSQL queries with Redis caching.
•Optimized for mobile and desktop viewing.

Engineering Optimizations & Patterns

Beyond the basic flow, this pipeline includes several engineering optimizations to keep it fast, reliable, and affordable in production.

Performance

•Async scraping and micro-batching to keep scrapers I/O-bound and maximize jobs-per-minute.
•Pre-aggregated views and Redis cache for fast 'top X' queries.

Reliability

•Idempotent ETL steps and Airflow retries so failed runs can be safely re-executed.
•Multi-stage validation from raw → enriched → gold → database load.

Cost & Scalability

•Batch AI enrichment to reduce OpenAI cost per job while improving throughput.
•Resource-aware scheduling that staggers heavy scraping and ETL jobs to fit on limited hardware.

Want to see this pipeline in action?

Explore real-time analytics and insights from the MENA job market