Director, Data and AI Enablement (Remote)

Date Posted:2026-06-24 Country:United States of America Location:US-CT-REMOTE Position Role Type:Remote U.S. Citizen, U.S. Person, or Immigration Status Requirements: Must be authorized to work in the U.S. without the company’s immigration sponsorship now or in the future. The company will not offer immigration sponsorship for this position.​ The company will not seek an export authorization for this role. Security Clearance Type: None/Not Required Security Clearance Status: Not RequiredAt RTX, the world largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world’s most complex problems.With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Pratt & Whitney is a world leader in the design, manufacture and service of aircraft engines and auxiliary power systems and has been revolutionizing modern flight for over 100 years. Join us and help shape the future of aerospace and defense.The Pratt & Whitney Digital and Analytics team has an immediate remote opportunity for a Director of Data and AI Enablement.What You Will Do:Our Digital and Analytics teams play a critical role in that mission by advancing the data, technologies, and digital capabilities that improve how we design, manufacture, and support the next generation of aerospace solutions.The Director of Data and AI Enablement is a global Digital Technology leader responsible for the data engineering, AI engineering, and foundational services that enable high-quality, accessible, and reliable data across the enterprise.This role focuses on making data from diverse sources—including IoT, field operations, and factory and production systems—ready, trusted, and usable for analytics, AI, and operational decision-making.This leader ensures data and AI engineering capabilities are modern, secure, reliable, and fully compliant with global trade, regulatory, export control, and data residency requirements, while avoiding duplication and enabling scalable enterprise use.The role partners across the full Digital Technology and business landscape to deliver unified and scalable data and AI capabilities for the company.Key Responsibilities: Enterprise Data & AI Engineering Strategy:- Set and lead the strategy for data engineering, AI engineering, and foundational data services, ensuring scalability, efficiency, and alignment to business outcomes.End-to‑End Data and AI Enablement Lifecycle:- Ensure an end‑to‑end lifecycle from diverse raw data sources—including IoT, field data, and factory/production systems—through engineering, modeling, deployment, monitoring, and operational insight.Data Architecture, Engineering, and Operational Integration:- Lead global teams across data architecture, data engineering, integration engineering, and data operations.- Establish and enforce standards for coding, observability, resiliency, SLIs and SLOs, incident response, and operational playbooks.- Align architecture and engineering practices with governance frameworks, domain models, security standards, and capability stability requirements.AI Capability Engineering, MLOps, and AI Ops:- Own global AI capability capabilities including compute environments, MLOps tooling, AI operational workflows, and model hosting capabilities.- Ensure AI environments meet standards for security, performance, availability, compliance, and cost optimization.- Deliver reusable AI capability components that streamline model development, deployment, monitoring, and audit readiness.Capability Convergence, Reusability, and Self Service Enablement:- Drive global convergence of data and AI capabilities, toolsets, engineering patterns, and operational processes.- Reduce fragmentation by standardizing build patterns, monitoring structures, SLAs and SLOs, and change management approaches.- Deliver reusable capability components, shared services, and repeatable patterns to maximize scale and reduce duplication.- Enable secure, governed self service for data, analytics, and AI so teams can build on standardized capabilities with confidence.- Ensure self service capabilities adhere to export control, global trade, data residency, and regulatory requirements.Retire redundant services and consolidate capabilities for operational simplicity and efficiency.Operational Excellence and Service Reliability:- Own reliability, availability, observability, and security across all data and AI capabilities.- Oversee global monitoring, incident management, problem management, capacity planning, disaster recovery, and lifecycle management.- Implement strong operational controls including access, change management, operational readiness reviews, and compliance enforcement.- Advance automation, SRE practices, and trend based operational improvements.Collaboration and Business Enablement:- Ensure capabilities support analytics, AI, operational workloads, transformation programs, and enterprise digital initiatives.- Partner with Data Science and AI and Data Ontology and Governance to align capability capabilities with modeling, metadata, lineage, and quality needs.- Support enterprise programs including SAP S4, global data products, digital transformation, engineering modernization, production systems, and sustainment operations.Leadership and Organizational Development:- Lead global teams of engineers, architects, SREs, capability operations professionals, and data operations specialists.- Foster a culture of engineering excellence, operational discipline, innovation, and continuous improvement.- Build organizational capability through workforce planning, skill development, mentoring, and global alignment.- Strengthen collaboration across engineering, operations, and business teams.Qualifications You Must Have:- Bachelor’s de