Director, Customer Success Data and Analytics 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 Youll 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 Were 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.

How Toast Uses AI in its Hiring Process

Throughout the hiring process our goal is to get to know you. We use AI tools to support our recruiters and interviewers with tasks like note-taking summarization and documentation of interviews to ensure they can be fully focused on your conversation. All hiring decisions are made by people. To learn more: Approach to Hybrid Working

We embrace a hybrid work model that fosters in-person collaboration while valuing individual needs. Our goal is to build a strong culture of connection as we work together to empower the hospitality community regardless of location. Please visit the Locations page on our career site to learn more about our in-office expectations by region: Equity and Inclusion is Baked into our Recipe for Success

At Toast our employees are our secret ingredientwhen they thrive we restaurant industry is one of the most diverse and we embrace that diversity with authenticity inclusivity respect and humility. By embedding these principles into our culture and design we create equitable opportunities for all and raise the bar in delivering exceptional experiences.

We Thrive Together

We embrace a hybrid work model that fosters in-person collaboration while valuing individual needs. Our goal is to build a strong culture of connection as we work together to empower the restaurant community. To learn more about how we work globally and regionally check out: today!

Toast is committed to creating an accessible and inclusive hiring process. As part of this commitment we strive to provide reasonable accommodations for persons with disabilities to enable them to access the hiring process. If you need an accommodation to access the job application or interview process please contact .

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