“Decoding Workforce Demand from Job Postings”
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Data Source: https://www.kaggle.com/datasets/asaniczka/1-3m-linkedin-jobs-and-skills-2024
Icon Source: Freepik.com/icon/working-time_12451584
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This project analyzes large-scale LinkedIn job postings to extract directional workforce demand signals related to job roles and skills. Using approximately 1.3 million job postings from 2024, the project treats job advertisements as observable expressions of employer intent, rather than as definitive indicators of hiring outcomes.
The analysis focuses on understanding how demand for roles and skills evolves over time and across locations, while explicitly accounting for common limitations of job posting data, including incomplete job descriptions, inconsistent job titles, and probabilistic skill extraction. Rather than assuming perfect data coverage, the project is designed to surface trends transparently and allow insights to be scoped based on data confidence.
The dataset consists of three primary components: job postings metadata, job summaries, and skills extracted via Named Entity Recognition (NER). These sources are integrated using a scalable Bronze–Silver–Gold architecture implemented in Databricks. Raw data is preserved in the Bronze layer, conformed and normalized entities are built in the Silver layer, and business-oriented analytical tables are curated in the Gold layer to directly support workforce intelligence questions.
Key analyses include:
Interactive dashboards are used to present these findings, enabling filtering by role, location, and time, and ensuring that users can interpret trends within clearly defined analytical boundaries.
To analyze how employer demand for job roles and skills evolves over time and across locations using LinkedIn job postings as observable market signals, while explicitly accounting for incomplete job descriptions and probabilistic skill extraction.
In 2026, workforce strategy, policy, and talent intelligence teams increasingly rely on large-scale job posting data as a proxy for employer intent rather than ground truth. Rapid role evolution, inconsistent job titles, and shifting skill requirements have reduced the reliability of traditional surveys and static occupational taxonomies.
In this context, LinkedIn job postings from 2024 are treated as observable demand signals that reflect how employers articulate hiring needs across time and geography. This project analyzes those signals to identify role-level and skill-level demand patterns, emerging and declining skill trends, and geographic variations, while transparently surfacing data coverage limitations caused by missing job descriptions and probabilistic skill extraction.
The objective is not to predict hiring outcomes or evaluate individual companies, but to provide directional, confidence-aware insights that support workforce planning, upskilling initiatives, and market intelligence in a volatile labor market.