Senior Director, Enterprise Data Management

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

• 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. 

$190,000 - $230,000 a year

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.