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.
About Toast
At Toast, we're building the restaurant platform that helps restaurants adapt, take control, and thrive. The Customer Success (CS) organization plays a pivotal role in helping customers get the most out of our products and we're transforming our data capabilities to drive a new era of proactive, data-informed customer engagement.
The CS Data & Analytics team is at the center of this transformation, building the data infrastructure that makes proactive, data-informed customer engagement possible at scale for ~150,000 restaurant locations.
Role Overview
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 an Analytics Engineer on the Customer Success Data & Analytics team, you'll bridge the gap between raw data and business-ready insights. You'll build the semantic layer, dbt models, and analytics datasets that power reporting, dashboards, and AI-driven workflows across the CS organization. Reporting to the Director of Data Infrastructure & Engineering, you'll work closely with Data Engineers, Analysts, and CS operations leaders to ensure data is not just available but trusted, consistent, and decision-ready.
This is a hands-on role focused on data modeling, metrics standardization, and analytics infrastructure, with a direct line to CS outcomes like customer retention, agent performance, and proactive customer engagement.
A day in the life (Responsibilities)
- Build and maintain dbt models that transform raw CS source data from systems like Salesforce, Five9, Intercom, NICE, and LevelAI into clean, analytics-ready datasets across CS domains including Customer 360, case management, omnichannel interactions, and agent performance.
- Design and own the semantic and metrics layer for CS KPIs including customer health scores, CSAT, case resolution rates, handle time, and retention signals, ensuring consistent definitions across all reporting surfaces.
- Partner with CS analysts and business stakeholders to translate reporting requirements into reusable, well-documented data models.
- Implement data testing, validation, and observability frameworks to ensure CS data assets are reliable and trustworthy at all times.
- Contribute to self-service analytics enablement by building datasets and semantic models in Snowflake and Sigma/Hex that allow CS analysts and operations leaders to answer questions independently.
- Collaborate with Data Engineers to ensure upstream pipeline outputs are modeled correctly and fit for purpose for analytics use cases.
- Establish and enforce data modeling standards, naming conventions, and documentation practices that scale as the team grows.
- Support AI and ML use cases by building clean, structured feature datasets for customer health scoring, churn prediction, and agent assist models.
- Participate in code reviews, design discussions, and technical architecture planning to continually raise engineering standards.
- 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.
What you'll need to thrive (Requirements)
- 5 or more years of experience in analytics engineering, data engineering, or a related role with a strong focus on data modeling and analytics infrastructure.
- Expert-level SQL and hands-on experience with dbt for building and maintaining data models in a cloud data warehouse environment.
- Experience with Snowflake or a comparable modern data warehouse such as BigQuery or Redshift.
- Familiarity with BI and analytics tools such as Sigma, Hex, Tableau, or Looker.
- Strong understanding of dimensional modeling, metrics layers, and analytics-ready dataset design.
- Ability to translate ambiguous business requirements from non-technical CS stakeholders into clean, scalable data models.
- Demonstrated ability to implement data quality testing, monitoring, and documentation as a standard practice.
- Demonstrated ability to communicate technical tradeoffs and data concepts clearly to non-technical stakeholders, including operations and business leaders.
- Collaborative working style with experience partnering across Data Engineering, Analytics, and business operations teams.
- Strong problem-solving skills, attention to detail, and a drive to build scalable, reliable systems.
What will help you stand out (Non-essential S