Senior Director, Enterprise Data Management

🕒 il y a 10 jours

🇺🇸 États-Unis – Télétravail

💵 $180 000 - $220 000 / an

⏰ Temps Plein

🟠 Senior

👔 Directeur

👻 Score fantôme 1%

Ce que ça veut dire : la probabilité que cette offre reste ouverte sans recrutement actif.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

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Versapay

Site WebLinkedInTous les Emplois

201 - 500 employés

Fondée en 2006

🤝 B2B

💳 Fintech

☁️ SaaS

💰 €4 000 000 Post-IPO Debt en 2019-11

B2B • Fintech • SaaS

Versapay est une entreprise qui propose un réseau collaboratif de gestion des comptes clients (AR), conçu pour offrir des solutions d'automatisation des AR et de paiements B2B de premier plan. La plateforme fournit des outils pour automatiser les tâches AR, gérer les applications de trésorerie et faciliter les paiements B2B sur plusieurs canaux, tout en s'intégrant avec des systèmes ERP tels que Microsoft Dynamics, NetSuite et Sage Intacct. Les solutions de Versapay aident les équipes à collaborer efficacement via le cloud, réduisant les traitements manuels et améliorant le flux de trésorerie. L'entreprise se concentre sur la simplification du processus de facturation-encaissement à l'aide de l'intelligence artificielle, améliorant l'expérience client avec des portails faciles à utiliser et offrant des méthodes de paiement numérique complètes. Versapay soutient également des transactions B2B à grande échelle dans le monde entier, promouvant l'efficacité et les bénéfices environnementaux grâce à la facturation numérique.

Description

• Define and drive Versapay’s enterprise data strategy, aligning the data roadmap to product, AI, and commercial objectives. • Lead the architectural convergence of transactional, operational, and analytical data layers into a unified, bi-directional operational backbone. • Own a multi-year data maturity roadmap across architecture, semantics, governance, and accessibility. • Establish canonical hierarchies, enterprise ontologies, and shared metric catalogues. • Operationalize data governance through automated classification, RBAC enforcement, platform SLAs, and certified data objects. • Formalize the Enterprise Data Catalogue and deploy an executive data health dashboard. • Enforce the data procurement gate and own the Enterprise Data Asset Registry. • Drive data infrastructure readiness for ML pipelines, LLM serving layers, and agentic serving tiers. • Establish schema contracts and semantic modelling standards for safe, scalable agent deployment. • Partner with Product and Engineering on reverse data flow, predictive model productization, and real-time data serving. • Govern sensitive data and external data products for quality, lineage, privacy, and compliance. • Evolve data requests into a governed, discoverable, product-oriented organization. • Operationalize external data products and partner with Commercial on data product commercialization. • Expand self-service data access while protecting compute capacity and governance standards. • Lead and grow the Data Platform Team in partnership with Embedded Analytics, Risk, and Compliance. • Bridge Product, Engineering, Commercial, Finance, and Legal/Compliance. • Build a culture of data discipline and represent the data function at executive level, partnering with the CTO.

🎯 Exigences

• 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. • 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.