Senior Director, Enterprise Data Management

What You’ll Do

Data Strategy & Architecture
• Define and drive Versapay’s enterprise data strategy, aligning the data roadmap to product, AI, and commercial objectives.
• Lead the architectural convergence of our transactional, operational, and analytical data layers into a unified, bi-directional operational backbone.
• Own a multi-year data maturity roadmap with clear milestones across architecture, semantics, governance, and accessibility.
•  Champion a business-first data modelling philosophy: canonical hierarchies, enterprise ontologies, and shared metric catalogues that allow humans and AI agents to interpret data consistently. 
Data Governance & Quality
• Operationalize data governance as a first-class concern — automated classification, RBAC enforcement, platform SLAs, and certified data objects.
•  Formalize the Enterprise Data Catalogue, replacing institutional knowledge with a searchable, self-service discovery layer.
•  Deploy and maintain an executive data health dashboard to provide ongoing visibility into the health and integrity of our data estate.
• Enforce the data procurement gate, ensuring new tools and systems are reviewed and classified before entering the estate.
•  Build and own the Enterprise Data Asset Registry to enable secure, frictionless data sharing internally and with commercial partners.
AI Enablement & Agentic Readiness
• Drive data infrastructure readiness to support Versapay’s AI roadmap — from ML pipelines and LLM serving layers
  to agentic serving tiers.
• Establish formal schema contracts and semantic modelling standards that guarantee deterministic outputs for safe,
  scalable agent deployment.
•  Partner with Product and Engineering to enable agentic capabilities: reverse data flow, predictive model productization, and real-time data serving.
• Govern data and AI exposure — ensuring sensitive data stays within approved platforms and all external data products meet strict quality, lineage, and privacy standards.
Data Accessibility & Commercialization
• Evolve the function from ad hoc data requests to a design-first, product-oriented organization with a governed, discoverable asset registry.
• Operationalize external data products for commercialization, delivering clear value to customers within consent
  and compliance frameworks.
• Partner with the commercial team on data product strategy — turning Versapay’s proprietary network data into defensible, recurring revenue.
• Expand self-service data access for internal teams while protecting compute capacity and governance standards. 
Team Leadership
•  Lead and grow the Data Platform Team, building a high-performing function with clear ownership across data strategy, engineering, consumption, AI compute; in tight partnership with the Embedded Analytics Team, Risk and Compliance.
•  Act as the cross-functional bridge between Product, Engineering, Commercial, Finance, and Legal/Compliance- ensuring data serves every function from a shared, trusted foundation.
• Build a culture of data discipline — standardizing how information is captured and governed so insight is consistent, discoverable, and trusted across the organization.
•  Represent the data function at the executive level, partnering closely with the CTO and contributing to the broader AI and product roadmap.

What You Bring

Required

• 10+ years of experience in data leadership, with at least 5+ years at the Director level owning enterprise data strategy, architecture, or governance.
• Demonstrated track record of modernizing and unifying complex, multi-source data environments at scale — ideally in a SaaS, fintech, payments context.
• Deep expertise in modern data stack: cloud data warehouses (Snowflake preferred), lakehouse architectures, ETL frameworks, and semantic/canonical modelling.
• Strong evidence of application of AI and ML infrastructure — including how data governance, observability, and semantic standards underpin safe, scalable AI deployment.
• Proven ability to build and lead high-performing technical teams and partner effectively across Product, Engineering, and Commercial functions.
• Experience governing data for commercial use: external data products, consent frameworks, lineage standards, and privacy compliance.
• Exceptional communication skills — able to translate complex data and architectural concepts for executive audiences and build alignment across functions.
• Experience with managing the cost of data warehouses and cost forecasting.
• Experience in hiring and managing talent across the entire data food chain – from BI and Analytics to Data Engineering to CI/CD of data platforms.

Preferred

• Experience with agentic AI architecture, MCP, or LLM serving layers and the data requirements that underpin them.
• Familiarity with AR/AP automation, payments, or working capital platforms and the data models they generate.
• Track record of commercializing data as a product — packaging data assets, building external APIs, or creating data-sharing programs with partners.
• Experience managing data maturity transformations with structured roadmaps, measurable milestones, and executive visibility.
• Hands-on experience with AWS-native data infrastructure (Glue, SageMaker, Bedrock) alongside Snowflake and modern BI tooling.
• Background in a PE-backed, high-growth SaaS environment.