Principal Engineer, Digital Innovation

Job Overview 综述

Celanese is seeking a highly skilled and experienced Principal Data Architect to drive data-led transformation. The ideal candidate will design conceptual, logical, and physical data models, ensuring they are reusable, easily understood, and future-proof. This role demands a deep understanding of business processes, data platform architecture, and data governance principles. Join our team to shape the future of data products, supporting strategic initiatives and enhancing data-driven decision-making across the organization. If you are passionate about creating robust data solutions and driving innovation, we invite you to apply.

Education and Experience

• Bachelor's or Master's degree in Computer Science, Information Technology, or related field.
• Minimum of 8 years of experience in database administration, systems engineering, and administration.
• Proven experience in at least one full implementation of data architecture solutions.

Technical Skills

• Expertise in data modeling, database management systems, data warehouses, and lakehouses.
• Proficiency in SQL development and database management.
• Strong knowledge of data modeling and designing tools such as ER/Studio.
• Experience with SAP, Salesforce, Informatica, Microsoft Azure, and Snowflake.
• Familiarity with cloud computing technologies and DevOps practices.
• Understanding of Generative AI, NLP, text analysis, predictive modeling, and machine learning.
• Knowledge of big data tools and data analysis techniques.

Methodologies and Best Practices

• Understanding of the systems development life cycle, project management techniques, design, and testing procedures.
• Ability to design and develop data flow structures and data warehouses.
• Skill in carrying out data security and compliance processes.

Soft Skills

• Critical thinking and problem-solving skills.
• Strong communication skills to effectively interact with cross-functional teams and stakeholders.
• Proficiency in agile methodologies.

Certifications

• Relevant certifications in data architecture, cloud computing, or database management are preferred.

• Ability to conduct research and develop strategies for data collection, migration, and retrieval.
• Understanding of data security measures for data systems.
• Experience in designing scalable, secure, and high-performance data architecture.

• Design and develop conceptual, logical, and physical data models.
• Ensure data models support business processes efficiently and are adaptable to future changes.

• Collaborate with stakeholders to gather and understand business requirements.
• Translate business requirements into technical specifications and data models.

• Optimize data integration, storage, and retrieval processes.
• Partner with Data Engineering to develop and maintain ETL processes.

• Partner with Data Governance to implement data governance principles to ensure data quality, consistency, and compliance.
• Develop and enforce policies and procedures for data management and usage.

• Work closely with cross-functional teams to ensure alignment of data architecture with business goals.
• Communicate complex technical concepts to non-technical stakeholders effectively.
• Support and Mentor Data Engineering and Data Governance team members

• Partner with leadership teams to design, develop, and implement organizational data strategy that aligns with business processes.
• Identify internal and external data sources to develop an effective data management system.

• Partner with Platform Engineering on all aspects of data architecture including platform evaluation, technical design, and application development.
• Utilize data modeling tools and programming languages to test and troubleshoot data structure applications and systems.
• Conduct continuous audits and performance measurements of database management.

• Integrate technical data functionalities that ensure data accessibility, security, and accuracy.
• Ensure compliance with regulatory and safety standards, securing data from potential threats.

• Document data pipeline processes for metadata and data cases.
• Define and manage data flow and dissemination within the organization.