AI Data Engineering Manager – GPSC Insights Engineering

General Motors

. Lead and develop a multidisciplinary team of data engineers, AI/ML platform engineers, software engineers, and technical leads .

Posted 11/2/2026full-timeAustin • Texas • United StatesSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in leading multidisciplinary teams in data engineering and AI/ML, with a strong focus on architecture, operational health, and scalable solutions. Proficient in establishing engineering standards, managing roadmaps, and driving MLOps practices to enhance data capabilities and deliver impactful AI applications.

Highest-signal resume keywords

Data Engineering LeadershipAI/ML Platform ArchitecturePython and SQL ProficiencyMLOps ImplementationCloud Platform Experience

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data EngineeringAI/ML EngineeringPythonSQLApache SparkKubernetesKafkaCI/CD StandardsData ArchitectureMachine Learning

Soft Skills

CommunicationInfluencingConflict ResolutionNegotiationTeam Building

Tools & Technologies

AzureAWSGCPLangChainCrewAIVertex AIFastAPIReact

Industry Keywords

Data GovernanceData QualityObservabilitySecurityPrivacyScalabilityIncident ResponseRoot-Cause AnalysisData ContractsAPIs

Tech Stack

Tools & technologies

ApacheAssemblyAWSAzureCloudCyber SecurityGoogle Cloud PlatformKafkaKubernetesPythonReactSparkSQL

About the role

Key responsibilities & impact

Lead and develop a multidisciplinary team of data engineers, AI/ML platform engineers, software engineers, and technical leads
Translate GPSC and Data organization strategies into an AI data engineering roadmap, operating plan, delivery commitments, and team objectives/KPIs
Own architecture, delivery, and operational health of scalable data and AI engineering capabilities across batch, streaming, and near-real-time workloads
Guide assembly, curation, and transformation of structured, semi-structured, unstructured, and multimodal datasets for AI/ML use cases
Set engineering standards for data contracts, schema management, lineage, metadata, data quality, observability, security, privacy, responsible AI, and reliability
Drive adoption of MLOps/LLMOps patterns including versioning, evaluation workflows, CI/CD, deployment automation, monitoring, drift detection, and retraining triggers
Set technical direction for AI application development, retrieval, evaluation, and orchestration
Establish reusable frameworks for source-system integrations across files, databases, APIs, event streams, suppliers, and supply-chain systems
Plan and allocate people, capacity, technology, and budget across the portfolio
Drive large projects spanning multiple departments while balancing performance, scalability, security, privacy, cost, and SLA/SLO attainment
Make and communicate technical decisions; resolve competing demands, conflicts, and tradeoffs
Build relationships with business and technology partners, data scientists, ML engineers, product owners, platform teams, cybersecurity, governance, suppliers, and vendors
Oversee production support, incident response, root-cause analysis, and preventative improvements
Prioritize work based on customer and partner needs and raise risks affecting business outcomes or customer experience
Lead change management and bounded experimentation with emerging AI, data, and engineering technologies
Build team capability through hiring, staffing, coaching, succession planning, performance management, recognition, and development
Create an inclusive, accountable, and psychologically safe team environment
Model General Motors behaviors around risk, sensitive information, ethics, urgency, collaboration, and enterprise impact

Requirements

What you’ll need

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 coaching, performance, staffing, prioritization, and delivery outcomes
Advanced understanding of data and AI platform architecture
Strong technical proficiency in Python and SQL
Working knowledge of Apache Spark and Kubernetes
Experience with Kafka or comparable streaming/event-driven technologies in Kubernetes-based environments
Experience with cloud platforms; Azure preferred, AWS or GCP also considered
Experience establishing 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
Experience setting objectives, managing roadmaps, allocating resources, tracking KPIs, communicating status and risks, and delivering across multiple teams or departments
Experience leading teams delivering AI applications or data products using 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
Master’s degree and the listed preferred qualifications are competitive advantages, not requirements
Must not require GM immigration sponsorship now or in the future; role excludes H1-B, OPT, STEM OPT, CPT, TN, J-1, and similar immigration support

Benefits

Comp & perks

Company vehicle evaluation program; eligible participants are assigned a General Motors vehicle to drive and evaluate after successful motor vehicle report review
Relocation benefits for candidates who qualify under company policy
Benefits supporting employee well-being at work and at home
Inclusive workplace and reasonable accommodation support