AI Data Engineering Manager - GPSC Insights Engineering
General Motors · Austin, Texas, United States
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 addresses gaps when results fall short.
- Establish mechanisms to understand customer and partner needs, use data and insights to prioritize work, and raise risks or issues that could negatively affect business outcomes or customer experience.
- Lead change management and bounded experimentation to evaluate emerging AI, data, and engineering technologies while protecting enterprise standards and operational stability.
- Build the team’s capability through hiring, staffing, career discussions, coaching, succession planning, performance management, recognition, and targeted development of technical and leadership skills.
- Create an inclusive, accountable, and psychologically safe environment where employees share ideas, challenge assumptions respectfully, learn from setbacks, and own outcomes.
- Model GM behaviors by speaking candidly about risks, protecting sensitive information, holding partners to ethical standards, moving with urgency, collaborating as one team, and considering enterprise impact in decisions.
Your Skills & Abilities (Required Qualifications)
- Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, Data Science, or a related field, or equivalent experience.
- 10+ years of experience in data engineering, software engineering, platform engineering, AI/ML engineering, or a related discipline, including production delivery of scalable data solutions.
- 3+ years of experience directly leading technical teams, including responsibility for coaching, performance, staffing, prioritization, and delivery outcomes.
- Advanced understanding of data and AI platform architecture, including data pipelines, data products, feature or embedding pipelines, model data delivery, and production operations.
- Strong technical proficiency in Python and SQL, with working knowledge of distributed data processing such as Apache Spark and container orchestration such as Kubernetes.
- Experience with streaming and event-driven data solutions using Kafka or comparable technologies, including operation in Kubernetes-based environments.
- Experience with cloud platforms; Azure preferred, with AWS or GCP also considered.
- Demonstrated experience establishing 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 data stores, object storage, APIs, and data governance.
- Working knowledge of machine learning and generative AI data lifecycle requirements, including training/evaluation data, feature engineering, embeddings, retrieval, inference, monitoring, and data/model drift.
- Experience setting objectives, managing roadmaps, allocating resources, tracking KPIs, communicating status and risks, and delivering results across multiple teams or departments.
- Experience leading teams that deliver AI applications or data products using AI development frameworks and services such as LangChain, CrewAI, Vertex AI, or comparable technologies.
- Working knowledge of RAG/vector search, FastAPI/React application patterns, LLM evaluation, and agent orchestration.
- Strong communication, influencing, conflict-resolution, and negotiation skills, with the ability to adapt recommendations for technical, business, and executive audiences.
What Can Give You a Competitive Advantage (Preferred Qualifications)
- Master’s degree in Computer Science, Software Engineering, Data Science, Business, or a related field.
- Experience managing managers, technical leads, or teams with diverse engineering and data/AI specialties.
- Experience with generative AI or LLM data platforms, retrieval-augmented generation, prompt and evaluation datasets, vector databases, embeddings, or knowledge retrieval systems.
- Experience with feature stores, model registries, experiment tracking, model serving, data/model monitoring, or other MLOps/LLMOps capabilities.
- Experience with Databricks, Snowflake, Azure Machine Learning, MLflow, or similar platforms.
- Experience designing ingestion and processing frameworks for heterogeneous and multimodal data such as documents, video, audi