Summary

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.

Description

The Global Sourcing and Supply Management (GSSM) Solutions team is seeking a Data Architect to lead complex, enterprise-scale data initiatives that directly power decision-making across commodity management, BOM analytics, pricing strategy, and supplier engagement. In this role, you will define scalable data strategies, build robust enterprise data models, and establish governance frameworks and reference architectures that shape how GSSM manages and leverages data to drive faster, better-informed sourcing and supply chain decisions. You’ll work closely with cross-functional teams and senior leadership to translate evolving business needs into high-impact, data-driven solutions, while applying AI and GenAI tools as a force multiplier across engineering, analysis and communication workflows.

Responsibilities

• Define and evolve the enterprise-wide data architecture strategy, including modeling, governance, metadata, lineage, and data access standards — with a clear focus on enabling rapid, data-driven decision-making across GSSM’s core domains: commodity management, BOM cost analysis, pricing, and supplier performance.
• Design and deliver reference architectures, reusable frameworks, and scalable patterns using ER modelling tools and modern data stack principles that make critical sourcing and supply data accessible, timely, and actionable.
• Collaborate across Supply Chain, Procurement, Finance, and Operations to deeply understand business problems - particularly around supplier engagement, cost optimization, and commodity risk and convert them into technical solutions that accelerate decision cycles.
• Provide strategic guidance and hands-on oversight of pipeline design, performance tuning, and data flow architecture, ensuring systems are scalable, secure, and optimized for the speed at which GSSM decisions need to be made.
• Lead architecture reviews and mentor engineering teams on best practices in data modeling, observability, reliability and compliance.
• Apply AI and GenAI tools strategically and effectively across the engineering lifecycle - from intelligent data profiling and automated metadata cataloging to AI-assisted pipeline development, anomaly detection, and rapid prototyping of data products. Demonstrate not just awareness of AI, but proven judgment in knowing where AI accelerates outcomes and where it does not.
• Work with product, analytics, and leadership teams to shape the data ecosystem in alignment with GSSM’s business strategy, global reporting needs, and the imperative for real-time, insight-driven supplier and commodity decisions.
• Champion data quality, governance, and platform usability, ensuring data systems are trusted, accessible, and aligned with privacy and compliance standards.
• Communicate complex data architectures, trade-offs, and insights to senior leadership and cross-functional stakeholders with clarity and precision - distilling technical complexity into simple, compelling narratives that inform executive decision-making.
  
  

Minimum Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or related technical field
• 5+ years of experience in data engineering
• 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 with a demonstrated understanding of how data architecture decisions directly impact business outcomes such as cost, speed-to-decision, and supplier negotiations.
• Hands-on understanding of pipeline architecture, observability, and system performance optimization
• Strong grasp of data privacy, governance, and security practices, including anonymization and compliance
• Demonstrated, practical experience applying AI and GenAI tools in real engineering workflows - not just familiarity, but a track record of using AI for code generation, data profiling, documentation automation, solution design, or intelligent testing. Must be able to articulate where AI added value and where human judgment was essential.
• Exceptional communication and stakeholder management skills, with proven ability to engage senior leadership, translate complex technical concepts into business language, and influence decisions through clear, concise, and visually effective presentations.
• Deep familiarity with GSSM-adjacent data domains - commodity analytics, BOM structures, pricing models, supplier scorecards, and procurement workflows - and an understanding of how timely, accurate data in these areas drives competitive advantage.
• Experience building data platforms that support real-time or near-real-time decision-making in fast-moving supply chain or sourcing environments.
• Experience with graph databases (e.g., TigerGraph, Neo4j) for modeling and visualizing iterative, multi-level BOM structures - including the ability to traverse complex parent-child component hierarchies, map supplier-to-part relationships, and build interactive BOM explosion/implosion views that support cost roll-up analysis and sourcing impact assessments.
• Exposure to machine learning, predictive analytics, and AI-powered decision-support systems, and their practical integration into data engineering pipelines.
• Experience with AI-assisted development tools and agentic workflows, with the ability to evaluate and adopt emerging AI capabilities that improve team velocity and solution quality.
• Strong storytelling and executive communication skills - able to take complex systems, data flows, or analytical findings and present them as simple, actionable narratives to diverse audiences including VP-level and above leadership.
• Track record of driving data literacy and data-informed culture within cross-functional teams.