Data Engineer

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other's ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you'll do more than join something - you'll add something. Are you an enthusiastic Data Engineer eager to apply your expertise in a fast-paced, innovative tech environment? Join our Global Sourcing & Supply Management (GSSM) Solutions team as a key player in revolutionizing our supply chain. The GSSM Solutions team is seeking a Data Architect to lead complex, enterprise-scale data initiatives. In this role, you will define scalable data strategies, build robust enterprise data models, and establish governance frameworks and reference architectures that shape how we manage and use data across the organization. You'll work closely with cross-functional teams to translate evolving business needs into high-impact solutions, while leveraging GenAI tools to accelerate engineering productivity and deliver modern, AI-ready platforms.

Description

Key Responsibilities - Define and evolve the enterprise-wide data architecture strategy, including modeling, governance, metadata, lineage, and data access standards. - Design and deliver reference architectures, reusable frameworks, and scalable patterns using ER modeling tools and modern data stack principles. - Collaborate across Supply Chain, Procurement, Finance, and Operations to deeply understand business problems and convert them into technical solutions. - Provide strategic guidance and hands-on oversight of pipeline design, performance tuning, and data flow architecture, ensuring systems are scalable, secure, and future-proof. - Lead architecture reviews and mentor engineering teams on best practices in data modeling, observability, reliability, and compliance. - Integrate Generative AI tools (e.g., for documentation, auto-generating metadata catalogs, and prototyping data flows) to accelerate engineering workflows and enhance productivity. - Work with product and analytics teams to shape the data ecosystem in alignment with GSSM's business strategy and global reporting needs. - Champion data quality, governance, and platform usability, ensuring data systems are trusted, accessible, and aligned with privacy and compliance standards.

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related technical field
• 8+ years of experience in data engineering, with 3+ years focused on data architecture or technical leadership roles in enterprise settings
• Strong expertise in data modeling (relational and dimensional), schema design, and enterprise data architecture patterns
• Deep knowledge of distributed data platforms and tools (e.g., Spark, Kafka, Iceberg, Flink, Airflow, Kubernetes, Snowflake, BigQuery)
• Proficiency in at least one language (e.g., Scala, Java, Python) and strong SQL skills

Preferred Qualifications

• Experience designing enterprise-scale data solutions across multiple business functions like supply chain, finance, and procurement
• Hands-on understanding of pipeline architecture, observability, and system performance optimization
• Strong grasp of data privacy, governance, and security practices, including anonymization and compliance
• Ability to integrate and apply GenAI tools into engineering workflows for prototyping, documentation, and solution design
• Excellent communication, collaboration, and stakeholder management skills across technical and non-technical audiences
• Familiarity with data environments in domains such as supply chain, procurement, or enterprise systems is a strong plus
• Exposure to machine learning and predictive analytics concepts and their practical use in data engineering pipelines
  Strong storytelling and executive communication skills, able to explain complex systems to diverse audiences

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