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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.
Job Responsibility:
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
Requirements:
6+ years in data engineering, data architecture, or BI, with strong hands-on technical depth
Proven experience or deep knowledge of architectures that power natural language querying over structured business data - including semantic layers, LLM-to-SQL patterns, and AI/BI platforms
Solid data modeling foundations - dimensional modeling, data contracts, metrics layers - and an understanding of how these underpin reliable AI outputs
Experience with modern data stack: Snowflake, dbt, ETL/ELT tooling
Experience with automation and integration platforms (WORKATO, e.g.)
Genuine excitement about the shift from traditional BI to conversational, agentic intelligence - and the technical judgment to do it right
Strong business acumen - able to understand GTM, CS, and Support workflows deeply enough to proactively identify where data and AI can create leverage
Proven track record of building trusted partnerships with business stakeholders, influencing roadmaps, and driving adoption of data solutions.
Genuine curiosity about how businesses operate, and the ability to ask the right questions before jumping to solutions.
Strong communicator who can bridge technical architecture decisions with business use cases for non-technical stakeholders.
Nice to have:
Hands-on experience building LLM-powered data applications (AI agents over structured and unstructured data)
Background in B2B SaaS, ideally cybersecurity or developer tooling
Knowledge of Salesforce data models and GTM analytics