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.
Own global AI capability capabilities including compute environments, MLOps tooling, AI operational workflows, and model hosting capabilities.
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.
Enable secure, governed self service for data, analytics, and AI so teams can build on standardized capabilities with confidence.
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.
Build organizational capability through workforce planning, skill development, mentoring, and global alignment.

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.
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.

Director, Data and AI Enablement

Key skills