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. This leader oversees enterprise data platforms, data engineering, enterprise data products, data quality engineering, database administration, and data operations capabilities that enable trusted, scalable, and secure data across the organization. The role leads teams that design, build, operate, and continuously improve data foundations for analytics, reporting, operational systems, predictive AI, and generative AI. In close partnership with business and technology stakeholders, the Director ensures data assets are reliable, discoverable, and ready to support enterprise data-driven decision-making. The Director sets strategy and roadmaps, establishes engineering and operational standards, develops enterprise data products, and ensures data platforms support analytics and AI. The role also leads and develops teams, manages budgets and resources, and cultivates technical excellence and continuous improvement.

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.
Develop and manage a multi-year roadmap aligned with business priorities and technology modernization goals.
Drive a data-as-a-product culture focused on trusted, reusable, business-aligned data assets.
Advise senior business and technology leaders on data platform investments and capabilities.
Establish goals, operating metrics, and performance measures that support continuous improvement and business value.
Lead the strategy, architecture, and operation of enterprise data platforms and engineering capabilities.
Set 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 best practices in data modeling, software development, testing, CI/CD, automation, and observability.
Evaluate and adopt technologies that improve engineering productivity, platform capabilities, and operational effectiveness.
Own the enterprise data product strategy and roadmap for foundational data assets.
Set standards for data product ownership, lifecycle management, reliability, discoverability, and operational support.
Partner with analytics, application, and business teams to develop reusable, scalable enterprise data products.
Enable self-service analytics and data consumption through consistent, trusted, well-documented data assets.
Measure data product success through adoption, quality, reliability, and business impact.
Ensure data platforms and products provide trusted foundations for analytics, predictive AI, and generative AI solutions.
Develop and operate feature-serving capabilities for machine learning and predictive modeling workloads.
Develop and operate context-serving capabilities for enterprise knowledge retrieval and generative AI experiences.
Partner with Data Science, AI Engineering, Architecture, and Analytics teams to support evolving 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 functions.
Establish enterprise 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 support approved enterprise policies, business definitions, and regulatory requirements.
Build, lead, and develop an organization of managers, engineers, and technical specialists.
Foster ownership, accountability, collaboration, and technical excellence.
Recruit, retain, and develop talent while creating clear career growth opportunities.
Manage team budgets, resource allocation, vendor relationships, and strategic investments.
Promote continuous learning and modern engineering practices across the organization.