About this role
Role Overview
This role is the architect and owner of the Customer Success data model at Toast. It is critical to our mission of building a data-driven culture across Customer Success, one where data is transparent, accessible, and trusted by the teams who depend on it.
The Customer Success data model needs to work reliably in two modes: as a structured foundation for dashboards, reporting, and operational metrics, and as a well-documented, trustworthy layer that AI systems can query consistently. Building for both and making deliberate architectural decisions about when each approach is appropriate is central to this role.
This is an embedded role, sitting inside the Customer Success organization. Customer Success data problems are business problems first. Understanding how customers are being supported, where friction exists, and what patterns predict risk or opportunity requires close proximity to the teams asking those questions. This role is positioned to build that context directly and translate it into data architecture decisions.
You will lead a small team of two senior data and analytics engineers to start, with room to grow as the function matures.
What You'll Do
Data Architecture & AI Data Strategy
• Own the redesign of the unified Customer Success data model, connecting data across Care, CX, Enablement, and CSS teams and source systems including Salesforce and the contact center platform.
• Define and execute the AI data strategy for Customer Success: specifically, how AI accesses, queries, and interacts with the data model. This means making active architectural decisions about when to pre-calculate and structure data versus when to allow dynamic AI retrieval, with predictability and consistency of outputs as the governing constraint.
• Build and maintain a documentation layer that functions as a first-class artifact. Reliable AI data access depends on well-structured, accurate documentation, and this person will treat it that way.
Platform & Pipeline Development
• Lead the data integration for the contact center platform migration, ensuring clean, well-modeled contact data flows into the Customer Success data layer from day one.
• Design and optimize pipelines for analytics, reporting, and AI/ML-driven use cases.
• Establish testing, monitoring, and alerting as standard practice across Customer Success pipelines: freshness checks, completeness validation, anomaly detection. Stakeholders should never be the first to know something is broken.
Leadership & Cross-functional Partnership
• Manage and technically mentor two senior data and analytics engineers, providing clear direction and building a high-performance team from the ground up.
• Serve as the primary data architecture partner for Customer Success analytics and operations leaders, translating business problems into data model and tooling decisions.
• Participate in cross-functional conversations