Principal Product Analytics Engineer

Toast

1d•$195,000 - $312,000

About The Position

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy. We are expanding our Product Analytics function and are looking for a Principal Product Analytics Engineer to define and build the technical foundation of our product analytics ecosystem. In this role, you will own the architecture for how product usage data is captured, structured, and made available for analysis across Toast’s ecosystem of mobile apps, web applications, Android OS software and hardware devices. A day in the life (Responsibilities) You will design systems and frameworks that power product telemetry, experimentation, analytics datasets, and AI-driven insights to enable product teams to understand user behavior and measure product impact. Working closely with Product, Engineering, Design, Data Platform, and Data Infrastructure teams, you will ensure product data is reliable, scalable, and built to support data-driven product development. You will use AI to deliver and improve the speed of insights into the product development lifecycle. The Product Analytics team sits inside of R&D Operations, which enables Toast’s product, design and engineering teams to build great products efficiently and at scale by providing the processes, insights and strategies to drive meaningful impact.

Requirements

Bachelor's degree in Computer Science, Information Systems, or a related field with a minimum of 12 years of experience in Data Engineering, Product Analytics, Data Analysis, Data Science or related field; or a Master’s degree with 8 years of related experience; or a PhD with 5 years of experience; or equivalent experience.
Experience working with large scaled product data from source to warehouse, specifically with web & mobile apps, Android OS software and devices.
Deep experience designing product analytics data infrastructure including schema design, ETL/pipelines, warehouses, and designing datasets for AI-driven analytics.
Extensive hands-on experience with Amazon S3 Data Lake, Airflow, dbt, Snowflake, Hex, Sigma, Amplitude, Python or similar tools. Expert level SQL and modeling skills, and experience with AI tools like Claude, Cursor, Chatgpt, MCP.
Proven ability to translate stakeholder needs and requirements into deployed scalable data architecture.
Experience supporting data foundations for experimentation and A/B testing measurement frameworks.
Strong cross-functional leadership with experience driving alignment across Product, Engineering, and Data teams.
Excellent communication skills, demonstrated through documentation and presentation to stakeholders and senior leadership.
A self-starter mindset, excited to build something from scratch and evolve it over time.

Responsibilities

Lead the architecture and design of Toast’s product analytics data ecosystem, spanning product telemetry, event pipelines, analytics-ready datasets, and semantic views.
Establish scalable data models that support product analytics, experimentation, executive reporting, and AI-powered insights.
Design frameworks for core analytics concepts such as sessions, identity resolution, feature usage tracking, and lifecycle metrics.
Develop and maintain reliable analytics layers of the product data warehouse using modern modeling frameworks.
Create reusable analytics datasets, standardized metrics, and canonical models used across product teams.
Implement automated data validation, monitoring, and quality controls to ensure reliable product data.
Partner with engineering teams to define and enforce product telemetry and instrumentation best practices.
Establish governance processes that ensure consistent metric definitions and high-quality data across domains.
Partner with analysts, data scientists and product managers to ensure the data foundation supports experimentation, dashboards, analytical studies, and AI workflows.
Serve as the technical authority on product data architecture, reliability and governance.
Mentor team members to help elevate the organization’s ability to leverage product data effectively.