This role at Apple involves joining a team focused on building modern, scalable data infrastructure to support analytics, machine learning, and AI-driven decision-making across Operations. The position requires a passion for creating reliable data systems and navigating the complexities of supply chain data. Candidates will engage with various teams to understand data needs and translate them into engineering solutions. The role emphasizes innovation and the application of advanced data engineering practices in a fast-paced environment.

• MS in Computer Science, Data Engineering, Statistics, Applied Math, Data Science, Operations Research or a related field and 8+ years of industry experience OR BS in related field with 10+ years hands-on industry experience
• Domain expertise in supply chain, operations management, logistics, planning & forecasting, production integration, and channel management
• Demonstrated expertise building and operating large-scale ETL/ELT pipelines using Python, SQL, and modern frameworks
• Proficiency with cloud data platforms and open table formats
• Strong command of advanced SQL for complex data modeling and query optimization
• Experience with workflow orchestration tools and building production-grade, monitored pipelines
• Hands-on experience implementing data quality frameworks and observability tooling
• Experience with data visualization and self-service analytics platforms
• Ability to work well in a fast-paced, iterative environment
• Exceptional ability to communicate complex data architecture decisions clearly

• Engage with business and analytics teams to understand data needs and translate requirements into engineering solutions
• Design and implement end-to-end data pipelines and architectures for batch and real-time workloads
• Build and maintain high-quality data models using modern transformation frameworks
• Architect and operate data workflows using orchestration tools with monitoring and alerting
• Implement data observability, lineage tracking, and validation frameworks
• Collaborate with Data Scientists, ML Engineers, and Software Engineers to operationalize models
• Partner with infrastructure teams to manage cloud-native data environments focusing on performance and scalability
• Leverage AI-assisted development tools to accelerate pipeline authoring and documentation
• Apply DataOps principles to deliver reliable, production-grade data products
• Champion a data product mindset to enable self-serve analytics and reduce bottlenecks
• Tune query performance and optimize storage for data at scale
• Develop and maintain clear technical documentation including data dictionaries and architecture decision records
• Present data infrastructure capabilities and architectural recommendations to senior leadership
• Research and evaluate emerging data engineering technologies to expand team capabilities

• Employees at Apple are often offered comprehensive benefits that support physical and mental well-being—flexible medical plans, confidential counseling, onsite wellness centers at major campuses, and resources for fitness and daily life. Families typically receive fertility support, paid parental leave with gradual return, caregiving leave, and dependent-care guidance, while financial perks commonly include stock grants (with purchase discounts), 401(k) matching, and income-protection coverage. Employees also see robust time off, Apple University learning and tuition reimbursement, donation matching and paid volunteer hours, and deep product and partner discounts.