Staff Data Engineer, Toast

• 5+ years of experience in data engineering, software engineering, or a related role building and maintaining data systems in production
• Strong SQL skills and experience with a programming language such as Python, Scala, or Java
• Hands-on experience with cloud-based data platforms (AWS, GCP, or Azure)
• Experience building and optimizing ETL/ELT workflows using tools such as Airflow, dbt, or similar orchestration frameworks
• Deep understanding of data modeling, data warehousing, and pipeline design
• Ability to write clean, maintainable, and well-tested code for data transformations and automation
• Demonstrated ability to communicate technical tradeoffs and data concepts clearly to non-technical stakeholders, including operations and business leaders
• Proven ability to work collaboratively with analytics, operations, and engineering teams
• Strong problem-solving skills, attention to detail, and a drive to build scalable, reliable systems

Desirable

• You'll be a founding member of a newly chartered data engineering function within Customer Success with a direct hand in shaping the architecture, tooling, domain model, and team culture from day one
• This is a rare opportunity to build something from greenfield, with visibility to VP and senior CS leadership
• As a Data Engineer on the Customer Success Data & Analytics team, you’ll design, build, and scale the data platforms and pipelines that power insights across our Customer Success organization
• Reporting to the Director of Data Infrastructure & Engineering, you’ll work closely with Data Infrastructure, Analytics, Engineering, and Product teams to ensure high-quality, reliable data is accessible and actionable
• This is a hands-on engineering role focused on delivering production-grade, scalable, and reliable data systems that support analytics, reporting, and operational decision-making across Customer Success
• Design, build, and maintain scalable, production-ready data pipelines (batch and real-time) for Customer Success using modern data technologies
• Develop and evolve data models and schemas that enable reporting, analytics, and machine learning across Customer Success functions
• Partner with CS operations leaders across care, workforce, and CX to translate business requirements into pipeline and infrastructure priorities, ensuring data work is directly connected to customer retention and operational outcomes
• Ensure data quality, observability, lineage, and governance are built into every data workflow
• Implement best practices for cost-efficient, performant cloud-based data infrastructure
• Contribute to data platform tooling and frameworks that support self-service analytics and reusability across teams
• Actively monitor and optimize data workflows for reliability, scale, and maintainability
• Collaborate with cross-functional stakeholders to define and track key metrics and ensure consistent data definitions
• Participate in code reviews, design discussions, and technical architecture planning to continually raise engineering standards
• Build and maintain semantic and metrics layer foundations (e.g., dbt metrics, Snowflake semantic layer) that enable consistent, reusable definitions of CS KPIs across reporting and analytics surfaces
• Contribute to the design and evolution of the CS data domain model including Customer 360, omnichannel interaction data, case management, and agent performance in close partnership with cross-functional stakeholders
• Partner with Toast's Business, Technology & Transformation (BTT) organization to establish data governance practices, security policies, and architecture standards that will serve as the foundation for CS data infrastructure. As a founding team member, you'll help define the norms, not just follow them
• Ensure data pipelines, storage, and access patterns adhere to BTT data standards, security policies, and compliance requirements
• Partner with BTT governance forums to align on design patterns, data architecture decisions, and SLA expectations
• Maintain documentation and metadata for all data assets in accordance with established governance processes