Director, Data & AI Engineering

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RTX

WebsiteLinkedInAll Job Openings

10,000+ employees

🚀 Aerospace

🎖️ Defense

🏭 Manufacturing

💰 $200k Grant - RTX on 2024-11

Aerospace • Defense • Manufacturing

RTX is a multinational aerospace and defense company that brings together aviation and defense businesses—including Collins Aerospace, Pratt & Whitney and Raytheon—to design, manufacture and service aircraft engines, auxiliary power units, integrated aerospace systems and defense solutions. The company focuses on advancing technologies that power flight, strengthen national security and support government and commercial customers worldwide, with a global presence across the Americas, Asia-Pacific, Europe, the Middle East and Africa. RTX emphasizes engineering, manufacturing, and research to solve complex technical challenges for aviation and defense.

📋 Description

• Define and lead the strategy for data engineering, AI engineering and core data services to ensure scalability, efficiency and alignment with business objectives. • Oversee end-to-end management of data from multiple sources (IoT, field data, manufacturing and production systems), including engineering, modeling, deployment, monitoring and operations. • Lead global teams responsible for data architecture, data engineering, integration and data operations. • Define and enforce standards for software development, observability, resilience, service level indicators (SLIs), service level objectives (SLOs), incident management and operational procedures. • Ensure alignment of architectural and engineering practices with governance frameworks, domain models, security standards and platform stability requirements. • Take global ownership of AI platforms, including compute environments, MLOps toolsets, operational processes for AI and model hosting. • Ensure AI environments meet security, performance, availability, compliance and cost-optimization requirements. • Develop reusable components that facilitate development, deployment, monitoring and compliance of AI models. • Promote global convergence of data and AI capabilities, tools, engineering practices and operational processes. • Reduce fragmentation by standardizing development methods, monitoring structures, SLAs/SLOs and change management approaches. • Build reusable components, shared services and reproducible patterns to increase efficiency and reduce duplication. • Establish secure, governed self-service capabilities that enable teams to use data, analytics and AI autonomously. • Ensure compliance with export controls, international trade, data residency and regulatory requirements. • Eliminate redundant services and consolidate existing capabilities. • Ensure reliability, availability, observability and security of data and AI platforms. • Oversee global monitoring, incident and problem management, capacity planning, disaster recovery and lifecycle management. • Implement robust operational controls for access, change management, operational readiness and compliance. • Advance automation, Site Reliability Engineering (SRE) practices and continuous operational improvement. • Ensure developed capabilities effectively support analytics, artificial intelligence, digital transformation initiatives and operational workloads. • Collaborate with Data Science, AI, Ontology and Data Governance teams to ensure platforms align with modeling, metadata, traceability and data quality needs. • Support major enterprise programs including SAP S/4HANA, global data products, digital transformation, engineering modernization, production systems and support activities. • Lead global teams of engineers, architects, SRE specialists, platform operations professionals and data operations staff. • Foster a culture of technical excellence, operational discipline, innovation and continuous improvement. • Build organizational capabilities through workforce planning, skills development, mentoring and alignment of global teams. • Strengthen collaboration between engineering, operations and business partners.

🎯 Requirements

• Bachelor’s degree in Computer Science, Management Information Systems, Information Technology or a related technical field, combined with a minimum of 14 years of experience in data engineering, platform engineering and platform operations; • OR • Graduate degree in a relevant field combined with a minimum of 12 years of experience in the same areas. • Experience leading large, global engineering and operations teams. • Deep expertise with cloud data ecosystems such as Databricks and Snowflake and their integration capabilities. • Experience with AI platforms, MLOps pipelines, model operations and high-performance compute environments. • Excellent understanding of data architecture, engineering patterns, observability, operational frameworks and platform security. • Demonstrated ability to translate business strategy into a technology roadmap and deliver scalable enterprise capabilities. • Strong influencing, communication and collaboration skills with technical and business leaders. • Proven experience in modernization, standardization, reliability engineering and operational excellence. • Track record of building and developing high-performing engineering and operations teams.

🏖️ Benefits

• Employer-contributed retirement and savings plan • Group insurance program • Career advancement opportunities (career progression) • Merit or recognition program • Health and wellness program, including telemedicine • Recreation and sports club • Nearby daycare services • Transit accessibility or public transit program and free parking