Senior Director, Enterprise Data Management

Job description

**About Versapay** Versapay is the platform that rewires AR by removing barriers to collecting and reconciling B2B payments, providing end- to-end cash flow clarity, ensuring businesses can manage working capital on their terms. By closing the loop for finance teams and their business systems, customers, and payment activity into a single intelligent ecosystem, Versapay transforms money matters into a data-driven advantage. With 10,000 customers and 5M+ companies transacting, Versapay facilitates 110M+ transactions and processes $300B+ in payments volume annually. **About the Role** We are looking for a strategic, builder-minded Senior Director of Enterprise Data Management to own and execute Versapay’s enterprise data strategy at a pivotal moment in our evolution. This leader will drive the convergence of our transactional, operational, behavioral, relational and intent data layers into a unified operational backbone — the foundational unlock for AI- powered product features, semantic data models, externalized data products, and autonomous AR workflows. This is a high-visibility, high-impact role directly tied to our product and commercial roadmap, with a clear mandate and executive alignment behind it. ### 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.