Building a Workforce Strategy for Leonardo

Discover How Draup Helped Leonardo Redefine Skill Architecture

60,000+

Employee Count

$20 Bn

Revenue

Aerospace & Defence

Industry

Rome, Italy

Headquarters

About the Company

A leading European multinational in aerospace, defence, and digital engineering, the company designs advanced systems in aviation, security, and space technology. The aerospace engineering giant also integrates advanced analytics, simulation, and cyber-resilient digital engineering to enhance competitiveness and drive workforce transformation in a rapidly evolving technological landscape.

The Core Challenges

Develop a unified framework to track role evolution across digital engineering,avionics, and cybersecurity, enabling forward-looking skills planning

Enhance visibility into digital talent ecosystems to benchmark peers and drive workforce redesign through build–buy–borrow optimization.

Design a learning strategy to equip employees with in-demand skills, driving immediate performance and long-term growth.

Expand visibility into software, and digital twin talent hotspots to support diversification and growth.

The Solution

Avionics Engineer

JOB POSTING TIMELINE

CORE SKILLS

TALENT MARKET INSIGHTS

TECH STACK

CAREER PATHS

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Skill Taxonomy
Expansion

Leveraged Draup’s ontology to expand the role–skill taxonomy, mapping STXM and high-school graduate pathways to Leonardo’s roles taxonomy while capturing emerging digital, cyber, and engineering capabilities.

Workforce Analytics Dashboard

Created a real-time skills intelligence dashboard, integrating HR data and global labor market trends, providing predictive insights on talent supply, automation impact, and learning priorities across geographies.

Personalized Learning & Career Pathing

Data-driven career path recommendations were implemented using the company’s internal data and Draup's global talent graph, with embedded learning modules aligned to daily tasks and upskilling needs.

Skill Gap & Readiness Analytics

Draup's Skill Gap Analyzer was utilized to evaluate readiness levels by function and region, and targeted interventions were delivered using the company’s Professional Training Center's training roadmap.

Outcome

Leonardo adopts a data-driven talent strategy with Draup, focusing on market intelligence, skill analytics, and individualized learning pathways to build a future-ready workforce.

Future-Ready Workforce Framework

Established a unified, forward-looking framework identifying emerging skill clusters in quantum engineering and simulation technologies while benchmarking capability evolution across peer ecosystems.

Enhanced Internal Mobility & Retention

Advanced talent matching enabled redeployment of engineers across global business units, reducing hiring costs and improving agility.

Smarter Learning Investments

Personalized learning and impact analytics shifted focus from activity-based training to measurable learning ROI.

Data-Driven Workforce Planning

Real-time analytics empowered HR and leadership teams to forecast role evolution and align planning with technological innovation.

Powered by unparalleled global labor & market data

23K+

Skills

1B+

Professionals

5700+

Locations

1.6M+

Peer Group Companies

1B+

Job Descriptions

4M+

Career Paths

Data Trust & Methodology at Draup

Draup uses a structured methodology to reduce inaccuracies and prevent misleading information, with a focus on transparency and responsible data practices.

1

Data Transparency and Coverage

Draup sources data from diverse, vetted global inputs including public datasets, labor information, professional ecosystems, and industry research. Coverage varies by region and role, and we supplement limited areas with alternative sources and analyst-led modeling.

2

Profiles Behind the Numbers

All aggregate insights related to buyers, roles, skills, and locations are grounded in structured, anonymized human and organizational profiles rather than pure extrapolation. This ensures context, reliability, and protection of personal identities.

3

Data Hygiene and Integrity

We maintain a clean, accurate data environment by removing duplicates, outdated entities, and invalid records. Automated checks powered by ML models and analyst reviews work together to ensure clarity and accuracy for every entity, such as buyer, role, skill, technology, or company. Learn more

4

AI Governance and Bias Mitigation

Our governance framework combines automated evaluations with Human-in-the-Loop reviews. We use statistical checks, audits, and cross-source comparisons to reduce demographic and structural bias before insights reach the platform.

5

Compensation Guardrails

Compensation insights use blended, directional inputs such as aggregated ranges, benchmarks, modeled distributions, and market signals. We distinguish clearly between modeled and reported data and apply guardrails to prevent over-interpretation.

6

Hourly and Frontline Workforce Coverage

For hourly, frontline, and blue-collar roles where digital visibility is limited, we incorporate government datasets, localized labor information, and specialized partners to build a more complete and balanced view of workforce supply and demand.

Building a Workforce Strategy for Leonardo

About the Company

The Core Challenges

The Solution