Senior Director, Enterprise Data Management

About the company

The company 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, the company transforms money matters into a data-driven advantage. With 10,000 customers and 5M+ companies transacting, the company 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 the company’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 the company’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 the company’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 the company’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.