WHAT DOES A DATA MODELER DO?
Published: October 3, 2024 - The Data Modeler supports the development, implementation, and maintenance of metadata management systems to ensure data is congruent, reliable, and accessible. This position collaborates with IT teams to translate business requirements into comprehensive data models, including conceptual, logical, and physical structures. This role utilizes advanced technology to extract, analyze, and document data, recommending process enhancements to uphold data integrity.
A Review of Professional Skills and Functions for Data Modeler
1. Data Modeler Duties
- Data Modeling: Creating logical and physical data models and diagrams to support backend development of advanced data solutions.
- Team Collaboration: Working closely with a team comprised of Data Engineers, Data Architects, Data Scientists, and Business Analysts to create conceptual data models and data flows.
- Metadata Management: Documenting and maintaining data model metadata, including data definitions, data relationships/dependencies, data restrictions, and data security policies.
- Database Design: Working with Data Architects and Data Engineers to transform logical data models into physical database designs and creating DDL scripts for deployment.
- Data Mapping: Assisting Data Architects and Data Engineers in data mapping activities and data profiling.
- Research & Development: Conducting research and development and contributing to the long-term positioning of emerging technologies related to data modeling, metadata management, and data governance.
- Documentation: Documenting solutions by developing documentation, flowcharts, layouts, diagrams, and charts.
- Requirements Analysis: Analyzing and documenting business data requirements and creating functional/technical design documents and related documentation.
- Data Design: Designing and developing conceptual, logical, and physical level data models (both relational and dimensional).
- Abstraction: Driving the proper level of abstraction once explicit business entities are understood.
2. Data Modeler Details
- Risk Modeling: Join the Enterprise Cyber Modeling Team and drive the agenda for quantitative risk development.
- Mathematical Modeling: Develop mathematical models for complex adaptive systems from development through testing and validation to production.
- Data Analysis: Work fluently with a variety of information sources and connect detailed analysis of cyber data with qualitative feedback from users.
- Stakeholder Management: Present to and work with senior leadership and cross-functional teams to deliver on strategic and operational intent.
- Data Investigation: Process and investigate large, messy data sets of numerical and textual data.
- Data Integration: Integrate with external data sources and APIs to discover interesting trends.
- Data Visualization: Design innovative data visualizations to communicate complex ideas to customers or company leaders.
- Technology Impact Analysis: Investigate the impact of new technologies on the future of cybersecurity in digital banking and the financial world of tomorrow.
- Iterative Modeling: Take an iterative approach to modeling and determine what points of the model need flex (abstraction) versus what needs explicit fixed modeling.
- Data Architecture: Drive the conceptual, logical, and physical design of data models to ensure it meets business objectives.
- Data Standards: Promote standards and business rules that meet identified requirements for both Data Modeling and Data Quality.
- Physical Data Modeling: Apply Physical Data Modeling skills in highly complex, cross-functional situations.
- Database Design: Define tables, columns, keys, data types, validation rules, stored procedures, domains, and access constraints.
3. Data Modeler Responsibilities
- Metadata Management: Provide support for metadata management system development, implementation, and maintenance.
- Data Requirements Gathering: Work with the IT teams to understand data requirements.
- Data Analysis: Collect, analyze, and summarize data to support business decisions.
- Data Accessibility: Provide data that is congruent, reliable, and easily accessible by the user.
- Data Monitoring: Utilize tools to monitor and mass update data changes.
- Data Modeling: Translate business requirements into conceptual, logical, and physical data models.
- Model Development: Develop and maintain the logical and physical data models.
- Data Extraction: Use technology to extract and analyze raw data.
- Technical Documentation: Develop and maintain technical documentation regarding data.
- Process Improvement: Make recommendations for process improvements to support data integrity efforts.
4. Data Modeler Job Summary
- Data Auditing: Perform regular data audits.
- Data Migration: Build data migration and single-source strategies.
- Metadata Governance: Participate in the establishment of governance for metadata management across the enterprise.
- Data Integration: Ensure integration of project logical data model into enterprise conceptual data model.
- Customer Data Design: Work in partnership with data architects to design a common customer view of information.
- Data Cleansing: Oversee the design and implementation of data cleansing procedures.
- Business Environment Analysis: Work with project teams to understand the business environment to manage enterprise-wide information/data support systems.
- Data Research: Research internal and external data sources for new data and improved sources of data feeds.
- Team Leadership: Work on multiple projects/issues/enhancements as a team leader.
- Complex Project Management: Work on complex enterprise-wide projects/issues/enhancements.
- Enterprise Data Modeling: Design logical enterprise-wide data models.
- Master Data Management: Provide support for master data management, logical data, quality system development, implementation, and maintenance.
5. Data Modeler Accountabilities
- Data Modeling: Serve as a data modeler and define or document conceptual data models across enterprise master data, transaction data, and informational data.
- Team Collaboration: Work with the data team, including SMEs, database architects, and data stewards to develop conceptual models that will support data availability, performance, security, and quality objectives of a Digital Transformation program.
- Data Model Alignment: Ensure the proper alignment of the physical and logical data models with use cases and objectives.
- Enterprise Modeling: Work with business resources and data stewards to identify “TO BE” enterprise data models that represent the desired data state.
- Program Analysis: Analyze program needs and translate them into data warehousing and data mart requirements.
- Query Development: Design, develop, and deploy query parameters, layout, filters, and analytics for program information solutions.
- Conflict Resolution: Recognize and resolve conflicts between models, ensuring data models are consistent with the enterprise model (e.g., entity names, relationships, and definitions).
- Data Integrity: Communicate data integrity accuracy to the business, and escalate or communicate issues when necessary.
- Technical Guidance: Provide guidance on complex issues to other team members.
- Automation Support: Support the development of automated solutions that enhance the quality of enterprise data.
- Data Model Maintenance: Create and maintain conceptual, logical, and physical data models defining information requirements for data management and business intelligence purposes.