Director, Data Platform Engineering

Domino's Corporate•Ann Arbor, MI

About The Position

The Director, Data & AI - Data Engineering & Platforms defines, refines, and executes Domino's enterprise data strategy, data platform roadmap, and data engineering operating model. The role oversees enterprise data platforms, data engineering, enterprise data products, data quality engineering, database administration, and data operations to enable trusted, scalable, and secure organizational data. This leader directs teams that design, build, operate, and continuously improve data foundations supporting analytics, reporting, operational systems, predictive AI, and generative AI. In partnership with business and technology stakeholders, the Director ensures data assets are reliable, discoverable, and ready to support increasing demand for data-driven decision-making. The Director sets the strategy and roadmap for data platforms, engineering, operations, and enterprise data products; establishes engineering, reliability, security, quality, and operational standards; and enables reusable data products and self-service data consumption. The role also develops data-serving capabilities for machine learning and generative AI, leads data quality and operations, and builds a high-performing organization while managing budgets, resources, vendors, and strategic investments.

Requirements

Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field.
10+ years of experience in data engineering, data platforms, cloud data technologies, or enterprise data management.
5+ years of leadership experience managing engineering, platform, or technology teams.
Proven experience building and operating enterprise-scale data platforms and data products.
Strong expertise with cloud-based data platforms, modern data architecture patterns, and large-scale distributed data processing.
Experience leading data quality, operational support, and platform reliability initiatives.
Strong communication and stakeholder management skills, with the ability to influence technical and business audiences.
Demonstrated ability to translate business objectives into scalable technology solutions and roadmaps.

Nice To Haves

Experience leading enterprise data modernization or cloud transformation initiatives.
Experience enabling advanced analytics, feature serving for predictive AI, and context serving for generative AI solutions.
Familiarity with modern lakehouse architectures, data product operating models, and DataOps practices.
Cloud and data platform certifications.
Experience in retail, restaurant, digital commerce, or other high-volume consumer-focused industries.

Responsibilities

Define and evolve the enterprise data platform, engineering, and operations strategy, and develop a multi-year roadmap aligned with business priorities and technology modernization goals.
Promote a data-as-a-product culture and advise senior business and technology leaders on data platform investments and capabilities.
Establish organizational goals, operating metrics, and performance measures to support continuous improvement and business value.
Lead the strategy, architecture, and operation of enterprise data platforms and engineering capabilities.
Establish standards for platform reliability, scalability, performance, security, availability, and cost optimization.
Lead the design and delivery of scalable batch, streaming, and real-time data solutions.
Drive engineering practices for data modeling, software development, testing, CI/CD, automation, and observability; evaluate emerging technologies that improve productivity and operational effectiveness.
Own the enterprise data product strategy and roadmap, including standards for ownership, lifecycle management, reliability, discoverability, and operational support.
Partner with analytics, application, and business teams to develop reusable enterprise data products and enable self-service analytics and data consumption.
Measure data product success through adoption, quality, reliability, and business impact.
Ensure enterprise data platforms and products provide trusted foundations for analytics, predictive AI, and generative AI.
Develop and operate feature-serving capabilities for machine learning and predictive modeling, and context-serving capabilities for enterprise knowledge retrieval and generative AI experiences.
Partner with Data Science, AI Engineering, Architecture, and Analytics teams to support changing data consumption patterns.
Invest in metadata, lineage, observability, and data quality to improve trust in downstream analytics and AI solutions.
Lead Data Quality Engineering and Data Operations, establishing standards for monitoring, validation, reconciliation, issue management, and operational support.
Drive operational excellence through incident management, root cause analysis, automation, and continuous improvement; define and monitor service levels, operational KPIs, and platform health metrics.
Partner with business governance, security, compliance, and analytics stakeholders to ensure platforms support approved enterprise policies, business definitions, and regulatory requirements.
Build, lead, and develop an organization of managers, engineers, and technical specialists, fostering ownership, accountability, collaboration, and technical excellence.
Recruit, retain, and develop talent; create clear career growth opportunities and promote continuous learning and modern engineering practices.
Manage team budgets, resource allocation, vendor relationships, and strategic investments.