AI Data Engineering Manager - GPSC Insights Engineering

Job Description The Role Join our GPSC Insights Engineering team, responsible for ideating, incubating, and delivering new data solutions for global purchasing supply chain (GPSC), Supplier Quality (SQ) and our partners. As the AI Data Engineering Manager, you will lead a multidisciplinary team responsible for building and operating industrialized data assets, AI data products, and data platforms that support Business Intelligence, Advanced Analytics, machine learning, and generative AI use cases. You will own a distinct AI data engineering area, translate functional strategy into a clear team direction and roadmap, and ensure delivery of trusted, scalable, secure, observable, and fit-for-purpose data products. In this role within the GPSC Insights Engineering organization under the Data organization, you will lead individual contributors and, as appropriate, other technical leaders across complex initiatives spanning multiple teams and departments. You will combine advanced technical judgment with people leadership, workforce planning, resource allocation, stakeholder influence, operational accountability, and change leadership to deliver measurable outcomes aligned with GM and GPSC priorities. What You'll Do • Lead and develop a diverse team of data engineers, AI/ML platform engineers, software engineers, and technical leads; establish clear roles, objectives, priorities, and measures of success. • Translate GPSC and Data organization strategies into an actionable AI data engineering roadmap, operating plan, delivery commitments, and team-level objectives and key performance indicators. • Own the architecture, delivery, and operational health of scalable data and AI engineering capabilities across batch, streaming, and near-real-time workloads. • Guide the team in assembling, curating, and transforming structured, semi-structured, unstructured, and multimodal datasets for model training, validation, evaluation, feature generation, embedding generation, retrieval, and inference. • Set technical direction and engineering standards for data contracts, schema management, lineage, metadata, data quality, observability, security, privacy, responsible AI, and platform reliability. • Drive adoption of repeatable MLOps/LLMOps patterns, including data and model versioning, experiment and evaluation workflows, CI/CD, deployment automation, monitoring, drift detection, and retraining triggers. • Set technical direction for AI application development, retrieval, evaluation, and orchestration capabilities, ensuring the team applies appropriate enterprise patterns. • Establish policies, processes, and reusable frameworks for source-system integrations from files, database extracts, APIs, event streams, external suppliers, and supply-chain systems. • Plan and allocate people, capacity, technology, and budget across the team’s portfolio; reprioritize and reallocate resources as business needs, risks, and opportunities change. • Drive strategy and implementation of large projects that impact multiple departments, balancing performance, scalability, reliability, resiliency, security, privacy, cost, and SLA/SLO attainment. • Make and communicate key technical decisions within the managed area; resolve competing demands, sensitive situations, conflicts, and tradeoffs within the organization and with partner teams. • Build strong relationships with senior business and technology partners, data scientists, ML engineers, product owners, platform teams, cybersecurity, governance, suppliers, and vendors; use persuasion and negotiation to gain alignment on new approaches. • Oversee production support, incident response, root-cause analysis, and preventative improvements for data and AI platforms; ensure the team meets commitments and addre