Director, Customer Success Data Engineering

Role Overview

This role serves as the data leader and technical authority for Customer Success data at Toast. You will own the strategy and execution of CS data modeling initiatives -- redesigning the CS data model from the ground up and defining how data is accessed, queried, and served across the organization.

The CS 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 CS organization. CS 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 CS data model, connecting data across Care, CX, Enablement, and CSS teams and source systems including our new contact center platform.
• Define and execute the AI data strategy for CS -- 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 -- not an afterthought. Reliable AI data access depends on well-structured, accurate documentation, and this person will treat it that way.
• Develop and apply a clear framework for when AI is the right tool versus traditional data approaches. Part of the job is pushing back when AI is unnecessary or introduces reliability risk.

Platform & Pipeline Development

• Lead the data integration for the contact center platform migration, ensuring clean, well-modeled contact data flows into the CS 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 CS pipelines -- freshness checks, completeness validation, anomaly detection. Stakeholders should never be the first to know something is broken.

Leadership & Cross-functional Partnership

• Manage and 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 CS analytics and operations leaders, translating business problems into data model and tooling decisions.
• Collaborate closely with cross-functional peers in CS analytics, enterprise data infrastructure, finance, and business technology to align on KPI definitions, data lineage, and governance standards.
• Represent CS data interests in enterprise governance forums, ensuring security, compliance, and architecture standards are met.

Enablement & Governance

• Ensure CS data is accurate, accessible, and trusted by the teams who depend on it -- from strategic OKRs down to operational dashboards.
• Maintain 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

• 10+ years of experience in data platform, data modeling, or analytics engineering roles, with at least 3 years in a leadership capacity.
• 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.
• A track record of treating pipelines like software: automated tests, schema validation, freshness and completeness monitoring, and changes that go through review before they reach production. You have been burned by silent data failures and build accordingly.
• Proven ability to diagnose systemic data quality and architecture problems and redesign from the ground up -- not just 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.
• Experience with BI tools and dashboarding.
• 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.