Data & AI Delivery Lead

What Timken makes possible begins with you.
Those who came before us helped land a man on the moon, create the world's infrastructure, and introduce renewable energy alternatives. Now you can join the Timken team to write your own unique story and help drive what's next.

A career at Timken means you can have an immediate impact doing Work That Matters to the world— improving the efficiency of today's industrial equipment and preparing for the future of motion on our planet and beyond. New employees can start contributing right away, and there are many opportunities to advance your career at your own pace. Join our global team of 19,000 people in 45 countries, and start helping our customers push the limits of what's possible in their world of motion.

JD - Data & AI Delivery Lead 

The Data & AI Delivery Lead is responsible for driving end-to-end delivery of Data Engineering, Analytics, and AI/ML solutions across the enterprise. This role oversees project execution, technical governance, solution quality, and cross-functional alignment to ensure that Data & AI initiatives deliver measurable business value. The leader works closely with Data Engineering, AI Engineering, Integration, Product Owners, and Business Stakeholders to plan, deliver, and mature Timken’s Data & AI capabilities.

Required Skills & Qualifications

• 15+ years of experience in Data Engineering, Analytics, AI/ML, or related technology fields.
• 5+ years leading data or AI delivery programs.
• Strong expertise in modern cloud data platforms (Azure preferred).
• Hands-on understanding of ETL/ELT pipelines, data modeling, MLOps, and AI engineering practices.
• Experience managing cross-functional and multi-location technical teams.
• Strong communication, leadership, and stakeholder-management skills.

Preferred Qualifications

• Experience with SAP-integrated data landscapes (e.g., SAP BI/BW, S/4, IBP).
• Experience in deploying GenAI solutions in enterprise environments.
• Certification in Azure Data/AI, PMI, Agile/Scrum, or related fields.
• Experience working with external partners or system integrators.

Key Responsibilities

1. Delivery Leadership

• Lead the planning and execution of Data & AI projects from initiation through deployment.
• Prioritize and allocate team resources across AI Engineering, Data Engineering, Analytics, and Integration workstreams.
• Ensure delivery timelines, quality, and scope adherence across multiple projects.
• Drive agile delivery frameworks, sprint planning, and execution governance.

2. Technical Oversight & Architecture Alignment

• Oversee solution designs related to Data Engineering (Data Lakehouse, ETL/ELT pipelines), AI/ML models, GenAI applications, and analytics dashboards.
• Ensure alignment with enterprise architecture, security policies, and data governance standards.
• Provide guidance on technology stack usage (Azure Data Services, AI/ML platforms, integration frameworks, Power BI, SAP BI/BW, etc.).

3. Data Governance & Platform Maturity

• Partner with Data Governance leaders to enforce standards for data quality, metadata, lineage, and access controls.
• Support the development of reusable data and AI building blocks to improve platform efficiency and reliability.
• Drive adoption of best practices across data‑model lifecycle management, MLOps, and responsible AI principles.

4. Stakeholder & Cross‑Functional Collaboration

• Engage with business stakeholders to understand requirements, define solutions, and ensure adoption.
• Work closely with Product Owners, Architects, Engineering Managers, and external partners/vendors.
• Communicate progress, risks, and insights to leadership and executive teams.

5. Team Leadership & Capability Development

• Manage and mentor a multidisciplinary team comprising Data Engineers, ML Engineers, Data Analysts, and Integration Engineers.
• Upskill and build internal capabilities in advanced analytics, AI/ML, GenAI, automation, cloud data engineering, and integration.
• Promote a culture of continuous improvement and innovation.

6. Operational Excellence

• Establish and monitor KPIs for platform performance, data reliability, and model effectiveness.
• Ensure adherence to compliance, audit, and security standards.

All qualified applicants shall be treated equally according to their individual qualifications, abilities, experiences and other employment standards. There will be no discrimination due to gender or gender identity, race, religion, color, national origin, ancestry, age, disability, sexual orientation, veteran/military status or any other basis protected by applicable law.