Across four structured phases — exploration, cleaning, statistical analysis, and visualization — HRIA’s EDA of 124,000+ LinkedIn job postings delivers a data-driven portrait of the tech and data talent market. These findings give DataTalent Solutions S.L. a defensible, evidence-based foundation for salary benchmarking, candidate sourcing strategy, and reskilling investment decisions. Understanding both what the data reveals and where its limits lie is essential to translating these results into reliable consulting outcomes.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/MajoRodri/HRIA/llms.txt
Use this file to discover all available pages before exploring further.
Salary Landscape
The salary distribution for data roles, after outlier removal, centers around a median of 128,596, reflecting a slight right skew (skew = 0.442) driven by a small concentration of high-compensation executive postings.| Statistic | Value |
|---|---|
| Median Annual Salary | $124,800 |
| Mean Annual Salary | $128,596 |
| Distribution Skew | 0.442 (right-skewed) |
| Salary Range (post-outlier removal) | 281,000 |
| Postings with Clean Salary Data | 6,108 of 19,725 (32.2%) |
Experience Level Premium
Pivot table analysis confirms a clear monotonic relationship between experience level and compensation. Executive and Director roles command the highest median salaries, while entry-level roles cluster at the lower end of the interquartile range near $90,000.- Executive / Director: Highest median salaries; widest variance due to equity and bonus structures in job descriptions
- Mid-Senior Level: The modal experience level in the dataset — represents the market’s core demand signal
- Entry Level: Concentrated at the IQR floor; still above national median wages, reflecting tech-sector premium
In-Demand Skills
Skill data in the LinkedIn dataset is structured into 35 broad skill categories, with IT and DATA emerging as the most demanded across all data-role postings. The top five categories account for the majority of skill tags across all postings. Key observations:- IT and DATA categories dominate across all data sub-roles (Data Engineer, Data Analyst, ML Engineer)
- Finance and Management skill categories appear frequently in senior and director-level postings
- Skill co-occurrence patterns suggest multi-disciplinary profiles are increasingly expected at mid-senior level
Top Industries
Data-role demand is not evenly distributed across the economy. The dataset captures 164,808 job-industry assignments across 422 distinct industries, but the distribution has a pronounced long tail.| Rank | Industry | Relative Share |
|---|---|---|
| 1 | Software Development | Highest |
| 2 | Financial Services / Finance | High |
| 3 | Healthcare / Hospital Systems | High |
| 4–10 | IT Services, Insurance, Consulting, Retail, etc. | Moderate |
| 11–422 | Long-tail industries | Minimal individually |
Work Type Distribution
Full-time employment is the overwhelmingly dominant contract structure for data roles, accounting for approximately 80% of all postings. Contract and part-time roles, while representing a smaller share, show higher salary variance — likely reflecting consulting-rate structures and project-based compensation.| Work Type | Share of Postings | Salary Variance |
|---|---|---|
| Full-time | ~80% | Moderate |
| Contract | Small | High |
| Part-time | Small | High |
| Other / Unspecified | Remainder | Variable |
Engagement Metrics
LinkedIn posting engagement metrics (views and applies) provide a secondary demand signal — but with significant structural limitations.| Metric | Median Value |
|---|---|
| Views per Posting | 5 |
| Applies per Posting | 4 |
Data Quality Summary
The following table consolidates the most consequential data quality issues identified across all four EDA phases. These findings must be disclosed in any external report or client deliverable derived from this dataset.| Issue | Severity | Affected Analyses |
|---|---|---|
| 67.8% of postings have no salary data (MNAR) | Critical | Salary benchmarks |
87.7% missing remote_allowed | High | Remote work analysis |
99.1% missing closed_time | High | Hiring velocity |
80.6% missing applies | Medium | Demand metrics |
23.7% missing formatted_experience_level (imputed) | Low | Experience analysis |
Imputation of
formatted_experience_level using job title keyword mapping recovered 23.7% of missing values with acceptable fidelity. All imputed values are flagged in the cleaned dataset and should be treated as estimates rather than ground truth in downstream analyses.Recommendations
Translate these findings into actionable talent sourcing, salary benchmarking, and reskilling strategies for DataTalent Solutions S.L.
Bias Overview
Understand the eight identified bias categories — MNAR salary, geographic, selection, gender proxy, and more — that constrain how these findings can be applied.