Data Modeler

A highly skilled Senior Data Modeller with over 10 years of experience in designing, optimizing, and governing enterprise-level data ecosystems. With deep expertise across conceptual, logical, and physical modelling, this professional plays a critical role in transforming complex business requirements into scalable and high-performance data structures.With a strong foundation in modern data platforms, database technologies, and architectural best practices, the Data Modeller ensures that data is accurate, consistent, secure, and readily available for applications, analytics, BI, and AI initiatives.

Essential Functions

• Developing Data Models: Design and develop conceptual, logical, and physical data models tailored to the DLH program requirements using Erwin Data Modeler.
• Requirements Gathering & Analysis: Collaborate with business analysts, data architects, and stakeholders to understand data needs, ensuring models accurately represent business processes and data flows.

Qualifications

• Bachelor’s Degree in Computer Science, Information Technology, Electronics, or related field
• Data Mapping & Metadata Management: Define and document data mappings, data definitions, and lineage within Erwin to facilitate data governance and quality.
• Supporting Data Governance & Standards: Ensure all data models comply with enterprise data standards, security policies, and regulatory requirements specific to the DLH environment.
• Facilitating Data Integration: Assist in designing models that support seamless integration of data sources into the Data Lake and Data Warehouse platforms.
• Impact Analysis & Change Management: Evaluate the impact of model changes on existing systems, and support change management processes to maintain data integrity.
• Documentation & Reporting: Generate comprehensive documentation, diagrams, and reports on data models, ensuring clarity for development, testing, and operational teams.
• Collaboration with Stakeholders: Work closely with data engineers, architects, and business teams for aligning data models with overall DLH architecture.
• Model Validation & Quality Assurance: Conduct reviews and validations to ensure the accuracy, consistency, and completeness of data models before deployment.
• Maintenance & Enhancements: Regularly update data models to reflect evolving business needs, system updates, and new data sources in the DLH environment.
• Supporting Data Lineage & Auditability: Enable tracking of data flow and transformations within the DLH program for compliance and audit purposes.