Enterprise Data Architect

Role Overview: As a Data Architect in the EDS segment, you will play a crucial role in designing and implementing enterprise data management systems to align with business needs and strategic goals. Your expertise will contribute to creating scalable, efficient data infrastructure that supports the future of electrified, automated, and connected vehicles. Key Responsibilities: - Design and implement comprehensive data architecture frameworks and data models - Develop data management strategies, governance policies, and quality standards - Create and maintain data dictionaries, taxonomies, and metadata repositories - Collaborate with stakeholders to understand business requirements and translate them into technical specifications - Establish data integration processes between disparate systems - Implement security measures to protect sensitive data - Evaluate and recommend appropriate data storage solutions (relational databases, data warehouses, data lakes) - Guide ETL processes and data pipeline development - Mentor data engineers and analysts on best practices Qualification Required: - Technical Skills: - Data Modeling: Expertise in conceptual, logical, and physical data modeling techniques - Database Technologies: Proficiency with relational databases, NoSQL solutions, data warehouses, and data lakes - Big Data Frameworks: Knowledge of Hadoop, Spark, Kafka, and other distributed processing systems - ETL/ELT Processes: Design and optimization of data integration pipelines - Data Governance: Implementing data quality, master data management, and metadata management - SQL & Query Optimization: Advanced SQL skills and performance tuning expertise - Cloud Data Platforms: Experience with AWS, Azure, or GCP data services - Data Security: Understanding of data protection, encryption, access controls, and compliance requirements - Strategic Skills: - Data Strategy Development: Aligning data architecture with business objectives - Enterprise Architecture: Understanding how data architecture fits within broader enterprise systems - Data Lifecycle Management: Policies for data retention, archiving, and disposal - Soft Skills: - Communication: Explaining complex data concepts to non-technical stakeholders - Collaboration: Working effectively with data engineers, analysts, scientists, and business teams - Problem-Solving: Addressing complex data challenges with innovative solutions - Documentation: Creating comprehensive data dictionaries, models, and architectural diagrams - Project Management: Coordinating data architecture initiatives across teams,