Skip to main content

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.

These recommendations are addressed directly to DataTalent Solutions S.L. — the HR consultancy commissioning the HRIA analysis — and to the analysts and consultants who will translate EDA findings into client-facing deliverables. Each recommendation is grounded in a specific finding from the four-phase EDA of 124,000+ LinkedIn job postings. Where the data has known limitations (MNAR salary, geographic skew, skill aggregation), those constraints are surfaced explicitly so that client communications remain accurate and defensible.

​
Talent Sourcing Priorities

The demand signal in the LinkedIn dataset is clear: mid-senior data engineers and ML engineers represent the highest-volume, highest-compensation sweet spot in the current tech talent market.
  • Prioritize mid-senior data engineering and ML engineering placements. These roles combine the highest demand volume (dominant in the IT and DATA skill categories) with a strong salary ROI that makes them compelling pitches for both candidates and hiring clients.
  • Target Software Development and Finance as primary industry partnerships. These two sectors dominate data-role postings and offer the richest placement pipeline. Establish preferred-partner arrangements or specialized practice areas for these verticals.
  • Align candidate pipeline to full-time roles. With ~80% of data-role postings classified as full-time, permanent placement should be the default service model. Contract and part-time desks are a secondary opportunity, particularly for senior and specialist profiles given their higher salary variance.
  • Use views-to-applies ratio as a role competitiveness proxy. High view counts paired with low apply counts may indicate a skills gap — a signal to proactively source passive candidates. High applies relative to views may indicate an oversaturated role where candidate differentiation advice adds value.
Build a simple competitiveness scoring model: competitiveness_score = applies / views. Roles scoring below the dataset median represent sourcing opportunities where DataTalent’s proactive outreach creates the most visible impact for hiring clients.

​
Salary Benchmarking

The HRIA salary figures represent a robust statistical baseline — but they require calibration before use in Spanish market consulting.
The HRIA salary benchmarks reflect primarily US market conditions. The LinkedIn dataset is geographically skewed toward North American postings. Applying these figures directly to Spanish client engagements without adjustment will systematically overstate market rates. Always disclose geographic scope in benchmark reports.
  • Do not apply US benchmarks directly to Spain. Adjust all figures using Spain-specific correction factors: local labor market surveys, INE (Instituto Nacional de Estadística) wage data, or Glassdoor Spain / InfoJobs salary reports.
  • For Spanish clients, clearly state that benchmarks are US-market-derived and provide a market-adjusted range alongside the raw HRIA figure.
  • Disclose the MNAR caveat in every salary report. Only 32.2% of data-role postings contain clean salary data, and the disclosing subset skews toward larger companies and more senior roles. Benchmarks represent the salary-transparent segment of the market, not the full population.
The following table provides approximate US market benchmarks by experience level, suitable for use as reference anchors before Spain-specific adjustment:
Experience LevelApprox. Median Salary (USD)
Entry Level~$78,000
Associate~$95,000
Mid-Senior Level~$124,800
Director~$175,000
Executive~$210,000+
When presenting salary ranges to Spanish corporate clients, apply an indicative correction factor of 0.55–0.70× the US benchmark as a starting point, then validate against current INE or Eurostat earnings data for ICT occupations. Always present the local figure as the primary benchmark.

​
Reskilling ROI

The EDA’s experience-level salary analysis provides a quantitative foundation for building the financial case for reskilling programs — a high-value service offering for DataTalent’s corporate clients.
  • Visualization 11 (salary uplift by role transition) demonstrates the compensation gain potential for junior professionals transitioning into data roles. Use this visualization directly in reskilling proposal decks.
  • Data Engineering and ML Engineering show the highest ROI for reskilling investment: the salary premium over entry-level analyst roles justifies the training investment within 12–18 months at typical Spanish training costs.
  • Visualization 10 (entry-level accessible roles) identifies Data Analyst and Business Intelligence Analyst as the most accessible entry points into the data career track — appropriate for early-stage reskilling cohorts with limited prior technical experience.
Recommended Reskilling Pathway: 
Tier 1 — Foundation:     Python + SQL fundamentals
    ↓
Tier 2 — Analytics:      Data Analysis → Business Intelligence
    ↓
Tier 3 — Engineering/ML: Data Engineering → ML Engineering
Structure client reskilling programs around this three-tier model. Tier 2 graduates become immediately placeable; Tier 3 completions command the salaries that generate the strongest ROI narrative for corporate sponsors.

​
Bias Mitigation Recommendations

Responsible use of the HRIA dataset requires explicit bias disclosures and supplementary data strategies. The following guidelines apply to all external client reports and any internal analyses that inform DataTalent’s pricing or candidate advice.
  • Never use raw LinkedIn salary data as an absolute benchmark without disclosing the MNAR caveat. The salary-reporting subset is systematically non-representative of the full market — it overrepresents larger employers, more senior roles, and US-headquartered companies.
  • Complement LinkedIn analysis with:
    • National labor surveys (INE, Eurostat, OECD Earnings Database)
    • Company-specific pay equity reports (for enterprise clients)
    • Salary negotiation data captured from DataTalent’s own recruiting interactions (proprietary signal)
  • For gender pay gap analysis: LinkedIn data alone cannot support this analysis. Partner with specialist survey providers (e.g., Mercer, Willis Towers Watson, Korn Ferry) who collect gender-disaggregated compensation data under appropriate legal frameworks. Do not attempt to infer gender from LinkedIn profile names or photos.
  • For Spanish market analyses: Always filter or subset by comp_country = 'ES' when working with salary fields. The current dataset’s comp_country distribution is dominated by US entries — the ES subset is small but substantially more relevant for domestic benchmarking.
Publishing salary benchmarks without disclosing the MNAR salary bias, the geographic skew toward the US market, and the selection bias toward larger companies creates material reputational risk. Every benchmark report must carry a clear methodology statement.

​
Data Collection Improvements

The most significant limitations of the HRIA analysis stem from structural gaps in the LinkedIn dataset. DataTalent can mitigate these by building its own complementary data assets.
  • Capture actual application counts. LinkedIn’s applies field records only Easy Apply submissions. Work with hiring clients to export total application counts from their ATS systems (Workday, Greenhouse, Lever, etc.) and reconcile with LinkedIn data to produce accurate funnel metrics.
  • Record remote/hybrid status explicitly. The remote_allowed field is 87.7% null. In all new client job briefs, make remote/hybrid/on-site classification a mandatory field. Build a proprietary tagging layer on top of LinkedIn postings using job description keyword extraction.
  • Collect gender-disaggregated pay data where legally permitted. Spain’s Royal Decree 902/2020 on pay equality requires companies of 50+ employees to conduct pay audits. Partner with HR compliance teams at enterprise clients to access these reports as a supplementary data source.
  • Track full posting lifecycle. The closed_time field is 99.1% null, making hiring velocity analysis impossible. Implement a crawler or API polling strategy to capture posting removal dates and compute time-to-fill metrics — a high-value KPI for DataTalent’s service reporting.
  • Build a proprietary dataset from client ATS systems. A longitudinal, consent-based dataset drawn from DataTalent’s own recruiting activity will provide ground-truth compensation and placement data that is geographically relevant, temporally current, and free of the selection biases inherent in public job board data.

​
Next Steps

1

Apply Spain-specific salary correction to all benchmark reports

Before publishing any salary benchmark derived from HRIA data to Spanish clients, apply market-adjustment factors using current INE ICT wage data or Glassdoor Spain / InfoJobs surveys. Document the adjustment methodology in the report appendix.
2

Implement the three-tier reskilling pathway

Launch or propose to corporate clients a structured reskilling curriculum: Tier 1 (Python + SQL foundations) → Tier 2 (Data Analysis / BI) → Tier 3 (Data Engineering / ML Engineering). Use Visualizations 10 and 11 as the ROI anchors in the sales deck.
3

Disclose MNAR, geographic, and selection biases in all published reports

Embed a standardized methodology statement in every client report that references HRIA data. This statement must disclose: (a) 67.8% MNAR salary missingness, (b) US geographic dominance, (c) selection bias toward large-company postings, and (d) applies undercounting.
4

Integrate government labor statistics for Spanish market validation

Cross-validate HRIA benchmarks against INE (Encuesta de Estructura Salarial), Eurostat earnings data for ICT occupations, and SEPE (Servicio Público de Empleo Estatal) occupation demand reports on a semi-annual basis.
5

Build a longitudinal dataset by repeating the LinkedIn crawl quarterly

Schedule quarterly LinkedIn data collection runs to track demand trends, skill category shifts, and salary evolution over time. Pair each quarterly snapshot with a DataTalent proprietary ATS data export to build a blended public + proprietary market intelligence product.
All numerical figures cited in this document — including median salary (124,800),meansalary(124,800), mean salary (128,596), the 67.8% MNAR salary missingness rate, the 87.7% remote_allowed null rate, and all experience-level salary approximations — are derived from the HRIA four-phase EDA of the LinkedIn Job Postings dataset (Kaggle, ~124K postings). These figures reflect primarily US market conditions and are intended as directional benchmarks, not definitive compensation standards for the Spanish or European labor market.

Key Insights

Review the full analytical findings: salary benchmarks, experience premiums, top industries, in-demand skills, and data quality conclusions.

Bias Overview

Understand the eight identified bias categories that shape how HRIA findings can — and cannot — be responsibly applied in client engagements.

Build docs developers (and LLMs) love

Get started for freeTalk to us