Who are we?
Checkmarx is the leader in application security and ensures that enterprises worldwide can secure their application development from code to cloud. Our consolidated platform and services address the needs of enterprises by improving security and reducing TCO, while simultaneously building trust between AppSec, developers, and CISOs. At Checkmarx, we believe it’s not just about finding risk, but remediating it across the entire application footprint and software supply chain with one seamless process for all relevant stakeholders.
We are honored to serve more than 1,800 customers, which includes 40 percent of all Fortune 100 companies including Siemens, Airbus, SalesForce, Stellantis, Adidas, Wal-Mart and Sanofi.
What are we looking for?
Director, Business Intelligence & AI to join our Business Applications organization. This is a senior individual contributor role with a director title and light team leadership (5 people), sitting at the intersection of data strategy, architecture, and execution. You'll own the data foundation that powers our company's most critical business decisions — from GTM and revenue analytics to operational intelligence across the organization.
You'll report to the VP of Business Applications and work closely with stakeholders across Finance, Sales, Marketing, Customer Support, Product, R&D, and HR to ensure our data assets are trustworthy, scalable, and increasingly AI-ready
What will you do?
· Architecting the data foundation that makes it possible for business users to ask questions in plain English and get trusted, real-time answers.
· Define and execute a strategy to migrate from traditional BI (dashboards, scheduled reports) toward AI-powered, natural language interfaces for business intelligence
· Architect the data layer that enables LLM-based querying — including semantic layers, vector stores, knowledge graphs, and metadata enrichment — so that AI agents can reliably reason over company data
· Design and own a scalable, AI-ready data architecture — warehouse/lakehouse structure, semantic layer, and data contracts — that supports both analytical and AI/ML workloads
· Build robust data modeling foundations (clean entities, clear metrics definitions, consistent taxonomies) that LLMs can reason over accurately
· Serve as a trusted advisor to business leaders across GTM, Customer Success, Finance, and Product — proactively engaging them to understand their decision-making needs.
· Translate ambiguous business questions ("why is churn spiking in the mid-market?") into well-defined data problems, and then into scalable, AI-powered solutions
· Build strong relationships with stakeholders at all levels — from individual analysts to VP and C-suite — and maintain a prioritized roadmap that reflects real business value
