Senior Manager, Customer Success Data and Analytics Engineering

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 with Finance and business technology partners on KPI definitions, data lineage, and governance standards, in close coordination with Customer Success senior leadership.

Enablement & Governance

• Ensure Customer Success data is accurate, accessible, and trusted by the teams who depend on it, from strategic OKRs down to operational dashboards.
• Contribute to a shared KPI dictionary, data lineage map, and self-service analytics framework in partnership with centralized data teams.
• Establish clear SLAs for data delivery and processes for ongoing data quality monitoring.

What We're Looking For

Required Qualifications

• 7+ years of experience in data platform, data modeling, or analytics engineering roles, with at least 2 years managing a team of data or analytics engineers.
• Demonstrated people management experience: you have directly managed data or analytics engineers, provided technical mentorship, and held a team accountable to delivery and quality standards.
• Experience designing data models with AI consumption in mind: structuring for retrieval versus pre-calculation tradeoffs, ensuring predictability of outputs, and understanding that documentation is infrastructure, not overhead.
• Hands-on experience with modern data stack tools (Snowflake, dbt, or equivalent), including building or maintaining a semantic layer that ensures consistent metric definitions across BI tools, notebooks, and AI agents.
• Proven ability to diagnose systemic data quality and architecture problems and redesign from the ground up rather than patch on top of what exists.
• Strong cross-functional communication skills: you can translate complex data architecture decisions for non-technical business stakeholders.

Preferred Qualifications

• Experience working in Customer Success, SaaS, or customer-facing operational analytics environments.
• Familiarity with Snowflake and modern orchestration frameworks (dbt, Airflow, or equivalent).
• Background that skews toward data modeling and transformation work: you think in terms of data models, schemas, and semantic layers rather than services and application infrastructure.
• Familiarity with real-time data streaming technologies (Kafka, Kinesis, or equivalent).

Who Will Thrive Here

• You are energized by greenfield work. This role is building something new, and the fingerprints you leave on the data model and AI strategy will be visible and lasting.
• You are comfortable with a lean team today. Two direct reports to start, with room to grow. This is not a role for someone who needs org size to feel impactful.
• You have the judgment to advocate for the right use of AI, including knowing when not to use it. The goal is a trustworthy, reliable data layer. Not AI for its own sake.
• You are a builder first. Strategy matters, but so does the ability to roll up your sleeves and make decisions at the implementation level.