Director, Data and AI Enablement (Remote)

RTX•US-CT-REMOTE, CT

1d•$186,200 - $353,800•Remote

About The Position

The Pratt & Whitney Digital and Analytics team has an immediate remote opportunity for a Director of Data and AI Enablement. 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.

Requirements

Bachelor’s degree in Computer Science, Management Information System, Information Technology or related technical field with a minimum of 14+ years of experience across data capabilities, data engineering, capability engineering, and capability operations; OR an advanced degree with a minimum of 12+ years of across data capabilities, data engineering, capability engineering, and capability operations.
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.

Nice To Haves

Experience leading large global engineering and operations organizations.
Deep expertise in cloud based data ecosystems such as Databricks, Snowflake, and associated integration capabilities and cloud native operations.
Experience owning AI capabilities, MLOps pipelines, model operations, and high performance compute environments.
Strong understanding of data architecture, engineering patterns, observability, operational frameworks, and capability security.
Proven ability to translate business strategy into technical roadmaps and deliver scalable enterprise capabilities.
Demonstrated influence and communication skills across technical and business leadership.
Track record of modernization, convergence, reliability engineering, and operational excellence.
Experience building high performing engineering and operations teams.

Responsibilities

Set and lead the strategy for data engineering, AI engineering, and foundational data services, ensuring scalability, efficiency, and alignment to business outcomes.
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.
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.
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.
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.
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.
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.
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.