Transforming job architecture for a $7.4B stock market exchange leader

Explore How Draup Built a Skills-Based Workforce Framework for a NY Exchange Leader

~9000

Employees

$7.4B

Revenue

Financial Services

Industry

New York, U.S.

Headquarters

About the organization

The company is a Stock Market Exchange Leader and a principal global fintech and capital markets infrastructure provider that operates major stock exchanges worldwide. It delivers advanced software, data, connectivity, and compliance technology solutions that underpin the stability and efficiency of global financial systems. With a rapidly evolving technology and regulatory landscape, the organization sought to modernize its workforce frameworks to remain competitive and agile.

The Core Challenges

Benchmarking Gaps
Align internal roles with market benchmarks to eliminate structural inconsistencies.
Static Growth Paths
Introduce dynamic, skill-driven pathways to support internal mobility and upskilling.
Siloed Intelligence
Connect Draup’s intelligence into existing systems for real-time workforce insights.

The Solution

Financial Analyst
CAREER PATHS
ROLE MAP
TOP EMPLOYERS
CORE SKILLS
SOFT SKILLS
The image is for representational purpose only.
01
Job Architecture Modernization
Aligned the company’s job taxonomy with Draup’s standardized framework through regular quarterly feed updates.
02
Skill-Based Pathing
Integrated comprehensive career pathing and reskilling insights into Workday via API connections.
03
Integrated Intelligence
Built an interactive Power BI dashboard to visualize skill gaps and career transition pathways in real time.
04
Ongoing Alignment
Enabled ongoing alignment with emerging roles, evolving skills, and dynamic market trends globally.

Outcome

Unified Job Architecture

Eliminated fragmentation across job families and aligned all roles with market benchmarks.

Dynamic Career Pathing

Empowered employees with visibility into realistic, skill-based growth opportunities.

Data-Driven Insights

Delivered real-time skill gap and transition analysis through interactive dashboards.

Continuous Market Alignment

Maintained up-to-date role structures through Draup’s quarterly taxonomy feeds.
6,100+​
Locations
4M+​
Career Paths​
1.7M+​
Peer Group Companies​
28K+
Skills
3,500+​
Roles
1B+​
Professionals​
55K+​
Universities​
1B+​
Job Descriptions​
200K+​
Courses​
180K+​
Technology Solutions​
1.6M+​
Account Priorities​
Labor Market Data Contextualized with Business Intelligence for Every Decision

Analyzing 1B+ data points
daily from 100K+ sources

6,100+​
Locations
4M+​
Career Paths​
1.7M+​
Peer Group Companies​
28K+
Skills
3,500+​
Roles
1B+​
Professionals​
55K+​
Universities​
1B+​
Job Descriptions​
200K+​
Courses​
180K+​
Technology Solutions​
1.6M+​
Account Priorities​
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SOC 2
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GDPR
ISO 27001 Logo
ISO
EAIGG Logo
EAIGG

Ethical AI, Backed by Global Standards

Draup, an AI-first company, upholds global standards like SOC 2, GDPR, and ISO 27001. As an EAIGG member, we audit for bias and build trustworthy AI.

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.

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.

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.

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.

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.

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.

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.

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