Data Engineer AI, Hybrid
IBM
A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You'll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you'll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.
As a Data Engineer specializing in Data Modeling, you will design and implement data models for applications and related systems, translating business needs into logical and physical data models. You will apply your expertise in data modeling tools and techniques to deliver high-quality solutions. Design Data Models: Design and implement data models for applications and related systems, ensuring alignment with business needs and requirements.
Apply Data Modeling Expertise: Utilize expertise in data modeling tools and techniques, including ERWin, MDM, and/or ETL, to develop logical and physical data models.
Translate business needs into logical and physical data models, ensuring data models meet business requirements and are properly implemented.
Implement Data Models: Implement data models for applications and related systems, ensuring data integrity and consistency across systems.
Master's Degree
Data Modeling Tools: Experience with data modeling tools such as ERWin, MDM, and/or ETL, with the ability to apply expertise in these tools to develop logical and physical data models.
Data Model Design: Experience with designing and implementing data models for applications and related systems, ensuring alignment with business needs and requirements.
Logical and Physical Data Models: Experience with developing logical and physical data models, translating business needs into technical solutions.
Data Integrity and Consistency: Experience with implementing data models that ensure data integrity and consistency across systems.
Business Requirements Analysis: Experience with analyzing business needs and requirements to inform data model design and implementation.
Preferred Technical And Professional Experience
Data Warehousing Knowledge: Exposure to data warehousing concepts and methodologies, including data governance and data quality, is beneficial for this role.
Advanced Data Modeling: Experience with advanced data modeling techniques, such as data vault and dimensional modeling, can be an asset in designing and implementing complex data models.
Big Data Technologies: Familiarity with big data technologies, including Hadoop and NoSQL databases, can be advantageous in developing scalable data models.
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