A Data Modeler ensures that an organization’s data is correctly organized and optimized for advanced analytics. This role involves defining, structuring, and organizing data elements within a system to ensure they are effectively utilized and easily interpreted by AI algorithms. By creating data designs for large datasets, the Data Modeler ensures high-quality, well-structured data for model training, validation, and deployment. Collaboration with Data Quality teams, Data Engineers, Data Scientists, and other stakeholders is crucial to develop and maintain proper data models.
Key Responsibilities:
Define Conceptual, Logical, and Physical Data Models:
- Conceptual Data Models: define the data architecture and define the relationships between different data entities
- Logical Data Models: define the structure of data elements and their relationships without focusing on physical database implementation.
- Physical Data Models: Define the actual implementation of the data in databases and storage systems to ensure AI systems have access properly formatted and stored data, enabling quickly and efficiently retrieve the data.
- Create Dimensional Modeling for AI Systems, organizing data into dimensions (such as time, geography, or product categories) that can be easily analyzed and queried to help machine learning algorithms quickly access relevant information and aggregate data in meaningful ways to improve the performance of AI models.
Build Data Modeling Enhances AI Readiness
- Structuring Data for Optimal AI Performance, to structure data in such a way that it can be easily used by AI algorithms, well-organized, labeled, and structured data to learn effectively to ensure that the data is consistently formatted and logically organized.
- Handling Big Data, designing efficient data model to support large-scale AI operations, ensuring data integrity, reducing redundancy, and optimizing storage space for batch, real-time or near-real-time data.
- Improving Data Flow and Storage, to define efficient data flow and storage to ensure that data moves smoothly between different systems, whether it’s being ingested from external sources, processed in real time, or stored for long-term analysis.
- Create a robust data model can help integrate these disparate sources into a unified system, ensuring that AI algorithms have seamless access to all relevant data without bottlenecks or delays.
Others
- Collaboration Between Data Scientists and Engineers
- Automation and Scalability in Data Modeling, ensuring that data models remain scalable and adaptable as the amount of data grows and evolves.
Qualifications:
- Bachelor’s degree in Computer Science, Computer Engineering, or relevant field
- At least 4 to 5 years of experience as Data Scientist, Data Engineer and Machine Learning or Deep Learning Operations)
- Technical skills: Data architecture Design, Normalization and Denormalized for structure and unstructured data. Data Warehousing and data Processes (Apache Spark, Hadoop, Apache Flink, and Google Cloud Platform to query and manipulate large datasets)
- Good communication skill
- Willing to be placed in Bintaro, Tangerang Selatan
A Data Modeler ensures that an organization’s data is correctly organized and optimized for advanced analytics. This role involves defining, structuring, and organizing data elements within a system to ensure they are effectively utilized and easily interpreted by AI algorithms. By creating data designs for large datasets, the Data Modeler ensures high-quality, well-structured data for model training, validation, and deployment. Collaboration with Data Quality teams, Data Engineers, Data Scientists, and other stakeholders is crucial to develop and maintain proper data models.
Key Responsibilities:
Define Conceptual, Logical, and Physical Data Models:
- Conceptual Data Models: define the data architecture and define the relationships between different data entities
- Logical Data Models: define the structure of data elements and their relationships without focusing on physical database implementation.
- Physical Data Models: Define the actual implementation of the data in databases and storage systems to ensure AI systems have access properly formatted and stored data, enabling quickly and efficiently retrieve the data.
- Create Dimensional Modeling for AI Systems, organizing data into dimensions (such as time, geography, or product categories) that can be easily analyzed and queried to help machine learning algorithms quickly access relevant information and aggregate data in meaningful ways to improve the performance of AI models.
Build Data Modeling Enhances AI Readiness
- Structuring Data for Optimal AI Performance, to structure data in such a way that it can be easily used by AI algorithms, well-organized, labeled, and structured data to learn effectively to ensure that the data is consistently formatted and logically organized.
- Handling Big Data, designing efficient data model to support large-scale AI operations, ensuring data integrity, reducing redundancy, and optimizing storage space for batch, real-time or near-real-time data.
- Improving Data Flow and Storage, to define efficient data flow and storage to ensure that data moves smoothly between different systems, whether it’s being ingested from external sources, processed in real time, or stored for long-term analysis.
- Create a robust data model can help integrate these disparate sources into a unified system, ensuring that AI algorithms have seamless access to all relevant data without bottlenecks or delays.
Others
- Collaboration Between Data Scientists and Engineers
- Automation and Scalability in Data Modeling, ensuring that data models remain scalable and adaptable as the amount of data grows and evolves.
Qualifications:
- Bachelor’s degree in Computer Science, Computer Engineering, or relevant field
- At least 4 to 5 years of experience as Data Scientist, Data Engineer and Machine Learning or Deep Learning Operations)
- Technical skills: Data architecture Design, Normalization and Denormalized for structure and unstructured data. Data Warehousing and data Processes (Apache Spark, Hadoop, Apache Flink, and Google Cloud Platform to query and manipulate large datasets)
- Good communication skill
- Willing to be placed in Bintaro, Tangerang Selatan