Americas Business Process Re-Engineering Data Engineer

Apple is where extraordinary people do their best work. If making a real impact excites you a career here might be your dream just be prepared to dream big. nnApples growing supply chain complexity demands innovative approaches beyond traditional data engineering. Youll join a team designing and building modern scalable data infrastructure that powers analytics machine learning and AI-driven decision-making across Operations. Youre passionate about building reliable data systems staying ahead of technology trends and thrive navigating ambiguity in a fast-paced environment. If this sounds like you wed love to

Engage with business and analytics teams to deeply understand data needs and translate requirements into robust scalable engineering solutions that directly impact Operations decisionsnDesign and implement end-to-end data pipelines and architectures from ingestion and transformation to delivery across batch and real-time streaming workloadsnBuild and maintain high-quality data models (dimensional relational or knowledge graph-based) using modern transformation frameworks such as dbt powering analytics and AIML use cases at scalenArchitect and operate data workflows using orchestration tools (e.g. Apache Airflow etc) with built-in monitoring alerting and SLA managementnImplement data observability lineage tracking and validation frameworks to uphold data integrity and trustworthiness across the platformnCollaborate with Data Scientists ML Engineers Software Engineers and Analysts to operationalize models and ensure data infrastructure supports production AIML workflowsnPartner with infrastructure and platform teams to manage cloud-native data environments (Snowflake Spark Delta Lake / Apache Iceberg) with a focus on performance cost efficiency and scalabilitynLeverage AI-assisted development tools (e.g. GitHub Claude) and LLM-powered agents to accelerate pipeline authoring code review documentation and transformation logic generation from natural language specificationsnApply DataOps principles including CI/CD pipelines version control automated testing and containerization (Docker Kubernetes) to deliver reliable production-grade data productsnChampion a data product mindset enabling self-serve analytics and reducing bottlenecks for downstream consumersnTune query performance partitioning strategies and storage optimization for data at scale in cloud warehouses and lakehousesnDevelop and maintain clear technical documentation including data dictionaries lineage diagrams and architecture decision recordsnPresent data infrastructure capabilities health metrics and architectural recommendations to senior leadership in clear non-technical termsnResearch and evaluate emerging data engineering technologies including streaming architectures GenAI-powered data tooling and next-generation warehousing to expand the teams capabilities and accelerate innovation

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 experiencenDomain expertise in supply chain operations management logistics planning u0026 forecasting production integration channel managementnDemonstrated expertise building and operating large-scale ETL/ELT pipelines using Python SQL and modern frameworks (dbt Spark Kafka/Flink for streaming)nProficiency with cloud data platforms (e.g. Snowflake) and open table formats (Delta Lake Apache Iceberg)nStrong command of advanced SQL for complex data modeling query optimization and analytics engineeringnExperience with workflow orchestration tools (Apache Airflow or equivalent) and building production-grade monitored pipelinesnHands-on experience implementing data quality frameworks observability tooling and data lineage tracking in production environmentsnExperienced with implementation and productionalization of GenAI and Agentic AI tooling including LLM-assisted code generation MCP servers and AI-powered data pipeline automationnExperience with data visualization and self-service analytics platforms (e.g. Tableau Streamlit ThoughtSpot) and the ability to build light front-end data productsnTrack record of staying current with industry best practices rapidly adopting emerging technologies (e.g. vector databases RAG pipelines AI-native data tools) and building functional prototypes to validate concepts

Ability to work well in a fast-paced iterative environment and deliver projects under timeline pressuresnChampion a culture of experimentation and continuous learning bringing innovative and strategic thinking to reporting business analytics and AI-powered automationnExceptional ability to communicate complex data architecture decisions clearly to both technical peers and non-technical senior stakeholdersnStrong interpersonal and collaboration skills to partner effectively across functions share knowledge and integrate diverse feedbacknSelf-sufficient with an ability to thrive in an environment of autonomy amidst ambiguity with a high bias for action and meticulous attention to data integrity