מציאת משרת הייטק בחברות הטובות ביותר מעולם לא הייתה קלה יותר
Apple Data Architect United States, West Virginia 729590780
04.09.2025
As the lead for enterprise data architecture, this role requires bridging business strategy, technical execution, and data governance. The ideal candidate must be capable of both high-level strategic planning and hands-on problem-solving.Key Responsibilities at Apple:1. Design and implement modern data infrastructure, ensuring secure and efficient operations.2. Drive the establishment of unified data standards and enterprise-level data modeling across business units (ensuring consistency, accuracy, availability, and security), including defining standards for data definition, format, collection, storage, flow, and usage, and enabling effective data governance based on these standards.3. Lead the modernization of data engineering platforms, promoting agile and self-service data analysis capabilities as well as interactive analytical capabilities for data agents.4. Drive digital and intelligent transformation with a core focus on data-driven business growth, identifying opportunities for bold innovation and experimentation.
- Business & Strategic Insight:
- Deep understanding of enterprise business models, core processes (e.g., supply chain, sales, risk control), and strategic goals, with the ability to align data architecture with business priorities.
- Data Architecture Design & Technology Selection:
- Expertise in enterprise data architecture frameworks (e.g., data warehouse, data lake, lakehouse, real-time data platforms), capable of designing logical architecture (data domains, model layers) and physical architecture (storage, computing, integration solutions).
- Familiarity with mainstream technology stacks (e.g., Hadoop ecosystem, cloud-native tools, real-time computing engines like Flink/Kafka, databases such as MySQL/PostgreSQL/NoSQL), and the ability to select and evaluate technologies based on business scenarios (e.g., high concurrency, massive data, real-time analytics).
- Data Governance & Compliance:
- Strong knowledge of data governance systems (data standards, metadata management, data quality, master data management, security, and privacy protection), with the ability to drive cross-functional rule-making and implementation.
- Familiarity with global and industry compliance requirements (e.g., GDPR, CCPA, China’s Data Security Law, Personal Information Protection Law, Level-3 Protection Scheme), ensuring architecture design meets compliance standards.
- Project Management & Cross-functional Coordination:
- Ability to break down architectural blueprints into actionable project plans, clearly defining phase goals, resource needs (people, budget), and risks (e.g., technical bottlenecks, business unit collaboration).
- Strong communication and influence skills to coordinate with business units (requirements), IT teams (technical implementation), and management (resource support), and resolve cross-departmental conflicts.
- Problem Solving & Systems Thinking:
- Capable of analyzing complex issues (e.g., data silos, system performance bottlenecks, data inconsistency) from a holistic perspective and designing systemic solutions rather than localized fixes.
- 6. Technical Vision & Iteration Capability
- Stay updated with emerging data technologies (e.g., AI-driven data processing, cloud-native architectures, Data Mesh), and assess their applicability to the enterprise to avoid outdated architecture.
- Adapt and evolve existing architecture based on business changes (e.g., new business lines) and technological advancements (e.g., lower-cost storage solutions), ensuring flexibility and scalability.
- Key Experience Requirements:
- Experience in Large-Scale Data Architecture Projects:
- Hands-on experience leading the full lifecycle of enterprise data platforms (e.g., data warehouse, data lake) from concept to delivery, including requirements gathering, design, development, deployment, and optimization.
- Familiarity with complex architecture scenarios (e.g., storing data for hundreds of millions of users, millisecond-level real-time queries, cross-region data synchronization).
- Cross-Industry or Multi-Domain Data Experience:
- Preferably with experience in target industries such as finance, retail, or manufacturing, understanding industry-specific data characteristics (e.g., risk control data in finance, user behavior data in retail).
- Experience integrating data across domains (e.g., business systems, logs, IoT, third-party data) is a plus.
- Data Governance & Team Leadership:
- Experience leading or participating in data governance initiatives (e.g., defining master data standards, building data quality monitoring systems), and driving governance rules into practice while managing resistance.
- Team leadership experience, capable of leading data architects, model designers, ETL engineers, and other roles to collaborate effectively and build team capabilities.
- Experience in Solving Complex Technical Challenges:
- Demonstrated experience in resolving major technical challenges (e.g., data consistency during migration, performance bottlenecks after system scaling, fault-tolerant design in distributed architectures).
- Familiarity with optimization strategies in large-scale data environments (e.g., database sharding, index design, caching strategies, task scheduling optimization).
- Cross-Organizational Collaboration & Resource Mobilization:
- Experience driving cross-functional data initiatives in large enterprises, balancing short-term business needs with long-term architectural integrity, and securing executive support for data strategy.
משרות נוספות שיכולות לעניין אותך
