Director, Data Engineering & Infrastructure (Customer Success)

Toast•Boston, MA

7h•$194,000 - $310,000

About The Position

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. Role Overview The Director of Data Infrastructure & Engineering in Customer Success will serve as the technical anchor for the Customer Success Data & Analytics organization, leading the strategy, architecture, and execution of all data infrastructure initiatives across CS. This leader will design and oversee the unified CS data model, ensuring consistent, reliable, and high-quality data across Care, Customer Success & Services (CSS), Enablement, and Customer Experience (CX) analytics domains. Partnering closely with Finance and Business Technology & Transformation (BTT), the Director will ensure that Customer Success data is accurate, governed, and available to drive decisions at every level; from strategic OKRs to real-time operational insights. The role will manage and mentor a small team of data and analytics engineers (and eventually data scientists), building scalable data pipelines, infrastructure, and analytics tooling to power a more data-driven CS organization.

Requirements

10+ years of experience in data engineering, data platform architecture, or data infrastructure, with at least 3+ years in a leadership role.
Proven track record in operating in cloud-based data ecosystems in complex, matrixed environments.
Demonstrated business acumen, with a bias toward iterative delivery, measurable impact, and value creation.
Strong expertise with modern data platforms (e.g., Snowflake, BigQuery, Redshift, Databricks, Delta Lake) and orchestration frameworks (e.g., Airflow, dbt).
Hands-on experience with programming and data languages (SQL, Python, Scala, or Java).
Demonstrated ability to align technical strategies with business outcomes and lead cross-functional stakeholder collaboration.
Experience managing and mentoring engineering teams, with a focus on performance, reliability, and scalability.
Excellent communication and influencing skills, particularly across business and technical stakeholders.

Nice To Haves

Experience working in Customer Success, SaaS, or customer-facing operational analytics environments.
Familiarity with data science workflows, model deployment, and machine learning infrastructure.
Expertise in real-time data streaming (Kafka, Kinesis, or Pub/Sub).
Experience building data governance frameworks and integrating with enterprise data management functions.
Strong understanding of financial data alignment, especially in collaboration with FP&A or Finance teams.

Responsibilities

Define and execute the data infrastructure vision and roadmap for Customer Success in alignment with enterprise data strategies and Toast’s technical standards.
Serve as a key bridge between CS, BTT, and Finance to ensure data alignment, quality, and accessibility across business domains.
Represent CS in enterprise governance forums and data councils, ensuring adherence to security, architecture, and compliance standards.
Build and mentor a high-performing data engineering and (future) data science team that balances innovation with discipline and governance.
Collaborate with analytics and operations leaders to translate business goals into data architecture, tooling, and workflow priorities.
Own the design and implementation of the unified CS data model, connecting data across CS teams (Care, CX, Enablement, CSS) and systems (Salesforce, Medallia, etc.).
Lead the development and optimization of data pipelines for analytics, reporting, and AI/ML-driven insights and tooling.
Drive the evolution of the CS data infrastructure to follow best in class practices and technologies, in alignment with the centralized data team’s best practices
Partner with the centralized BTT data architecture team to ensure platform integrity, metadata management, and observability.
Maintain transparency and rigor in KPI definitions and data lineage across CS and enterprise systems.
Establish processes for data quality monitoring, schema management, and SLAs for data delivery.
Contribute to and uphold a shared KPI dictionary, data lineage map, and self-service analytics framework.
Ensure ongoing compliance with data security, privacy, and governance requirements.
Enable data-driven decision-making across CS by building infrastructure that supports self-service analytics and operational automation.
Collaborate with analytics and data science pods to enable predictive insights (e.g., contact reduction, customer pain propensity, satisfaction scoring).
Lead innovation through AI-driven analytical methods including classification, NLP, and propensity modeling.
Identify and implement emerging technologies that improve data accuracy and usability.
Partner with CS teams to evolve real-time triggers and proactive customer engagement frameworks & tooling.