AI Data Engineering Manager - GPSC Insights Engineering

Description

The AI Data Engineering Manager joins the GPSC Insights Engineering team, which designs, prototypes, and ships new data solutions for global purchasing supply chain (GPSC), Supplier Quality (SQ), and partner groups.

You will lead a mixed team that builds and runs production data assets, AI data products, and data platforms for business intelligence, advanced analytics, machine learning, and generative AI.

You will own one AI data engineering area, turn functional strategy into team direction and a roadmap, and make sure data products are trusted, scalable, secure, observable, and fit for purpose.

The role sits in GPSC Insights Engineering within the Data organization. You will lead individual contributors and, where needed, other technical leads on work that spans several teams and departments. You will balance technical judgment with people leadership, workforce planning, resourcing, stakeholder influence, operations, and change leadership to meet GPSC and company priorities.

Responsibilities

• Grow a mixed team of data engineers, AI/ML platform engineers, software engineers, and technical leads by setting roles, goals, priorities, and success measures.
• Turn GPSC and Data organization strategy into an AI data engineering roadmap, operating plan, delivery commitments, and team objectives with KPIs.
• Own the design, delivery, and day-to-day health of scalable data and AI capabilities for batch, streaming, and near-real-time workloads.
• Direct how the team assembles, cleans, and transforms structured, semi-structured, unstructured, and multimodal data for training, validation, evaluation, features, embeddings, retrieval, and inference.
• Set standards for data contracts, schema management, lineage, metadata, quality, observability, security, privacy, responsible AI, and reliability.
• Push repeatable MLOps and LLMOps practice: data and model versioning, experiment and evaluation workflows, CI/CD, deployment automation, monitoring, drift detection, and retraining triggers.
• Set direction for AI application development, retrieval, evaluation, and orchestration, using suitable enterprise patterns.
• Create policies, processes, and reusable frameworks for integrating source systems: files, database extracts, APIs, event streams, external suppliers, and supply-chain systems.
• Allocate people, capacity, technology, and budget across the portfolio, and shift resources when needs, risks, or opportunities change.
• Lead large cross-department projects, balancing performance, scalability, reliability, resiliency, security, privacy, cost, and SLA/SLO targets.
• Make and explain key technical decisions in your area, and settle competing demands, sensitive issues, and tradeoffs inside the company and with partner teams.
• Build relationships with senior business and technology leaders, data scientists, ML engineers, product owners, platform, cybersecurity, governance, suppliers, and vendors, using persuasion and negotiation to align on new approaches.
• Run production support, incident response, root-cause analysis, and preventive fixes for data and AI platforms, and close gaps when results miss commitments.
• Set up ways to learn what customers and partners need, use data to rank work, and escalate risks that could hurt business outcomes or customer experience.
• Lead change and bounded experiments to assess emerging AI, data, and engineering tools without weakening enterprise standards or stability.
• Develop the team through hiring, staffing, career conversations, coaching, succession planning, performance management, recognition, and technical and leadership growth.
• Foster a team that is inclusive, accountable, and psychologically safe, where people share ideas, challenge assumptions respectfully, learn from setbacks, and own results.
• Lead by example: speak plainly about risk, protect sensitive information, hold partners to ethical standards, act with urgency, work as one team, and weigh enterprise impact.

Requirements

• Bachelor's degree in computer science, software engineering, data engineering, data science, or a related field, or equivalent experience.
• 10+ years in data, software, platform, or AI/ML engineering, or a closely related field, including shipping scalable data solutions to production.
• 3+ years leading technical teams, covering coaching, performance, staffing, prioritization, and delivery results.
• Deep understanding of data and AI platform architecture: pipelines, data products, feature and embedding pipelines, model data delivery, and production operations.
• Strong Python and SQL, plus working knowledge of Apache Spark and Kubernetes.
• Experience with streaming and event-driven data using Kafka or similar, including running it on Kubernetes.
• Experience with cloud platforms. Azure is preferred; AWS or GCP is also considered.
• Proven record setting engineering standards for CI/CD, infrastructure automation, testing, observability, incident response, security, and production support.
• Experience with data architecture, modeling, schema design, data contracts, relational and non-relational stores, object storage, APIs, and data governance.
• Working knowledge of the ML and generative AI data lifecycle: training and evaluation data, feature engineering, embeddings, retrieval, inference, monitoring, and data or model drift.
• Experience setting objectives, running roadmaps, allocating resources, tracking KPIs, reporting status and risk, and delivering across teams or departments.
• Experience leading teams that build AI applications or data products with frameworks or services such as LangChain, CrewAI, Vertex AI, or similar.
• Working knowledge of RAG and vector search, FastAPI and React patterns, LLM evaluation, and agent orchestration.
• Strong communication, influence, conflict resolution, and negotiation, with the ability to tailor messages for technical, business, and executive audiences.
• Preferred: Master's degree in computer science, software engineering, data science, business, or a related field.
• Preferred: Experience managing managers, technical leads, or teams spanning engineering and data or AI specialties.
• Preferred: Experience with generative AI or LLM data platforms, RAG, prompt and evaluation datasets, vector databases, embeddings, or knowledge retrieval.
• Preferred: Experience with feature stores, model registries, experiment tracking, model serving, data or model monitoring, or other MLOps and LLMOps tools.
• Preferred: Experience with Databricks, Snowflake, Azure Machine Learning, MLflow, or similar.
• Preferred: Experience building ingestion and processing for heterogeneous, multimodal data such as documents, video, audio, and images.
• Preferred: Knowledge of data observability, model and data drift, bias or fairness, explainability, and trustworthy AI controls.
• Preferred: Experience with workforce planning, operating budgets, vendor or supplier management, financial stewardship, and large technology change.
• Preferred: Experience with automotive, manufacturing, global purchasing, supplier, or supply-chain data.

Conditions

Hybrid: report to a specific location at least 3 times a week, or as your manager sets.

The company does not provide immigration sponsorship for this role. Do not apply if you will need company immigration sponsorship now or later, including direct sponsorship, the company as employer of record on a government form, or any work authorization that requires a written submission or other immigration support from the company, such as H-1B, OPT, STEM OPT, CPT, TN, or J-1.

Candidates may need to complete a role-related assessment and/or pre-employment screening before starting.

The company is committed to being free of unlawful discrimination and to fostering inclusion and belonging.

Benefits

• Eligibility for a company vehicle evaluation program after a successful motor vehicle report review, where you drive and evaluate an assigned company vehicle. Participants must buy or lease a qualifying company vehicle every four years unless a limited exception applies.
• Relocation benefits are available for candidates who qualify under company policy.
• Well-being support from day one, at work and at home, with details in the company's Total Rewards resources.