Description

Key Responsibilities

Enterprise Data Modeling

• Design, develop, and maintain conceptual, logical, and physical data models for enterprise data platforms, analytics solutions, and business applications.
• Translate complex business requirements, business processes, and data requirements into scalable and reusable data structures.
• Develop and maintain:
  • Relational data models
  • Dimensional data models
  • Star schemas
  • Snowflake schemas
  • Domain-oriented data models
• Define entities, attributes, relationships, primary keys, foreign keys, business keys, hierarchies, and reference-data structures.
• Design models that appropriately support transactional, analytical, historical, and reporting requirements.
• Perform model normalization and denormalization based on application, integration, analytics, and performance requirements.

Data Warehousing & Analytics Modeling

• Design dimensional models for enterprise data warehouses, data marts, and modern analytics platforms.
• Define facts, dimensions, measures, hierarchies, and grain based on business and analytical requirements.
• Design appropriate approaches for maintaining historical information, including Slowly Changing Dimensions (SCDs).
• Ensure models support reporting, BI, advanced analytics, and downstream data-consumption requirements.
• Partner with Data Engineers to ensure models can be effectively implemented within ETL/ELT and data-processing pipelines.

Data Standards & Governance

• Define and enforce enterprise data-modeling standards, naming conventions, data types, and modeling best practices.
• Partner with business and technical stakeholders to establish consistent definitions for critical business data.
• Support master data, reference data, metadata management, and data-governance initiatives.
• Identify authoritative data sources and help establish consistent definitions and relationships across enterprise systems.
• Contribute to the development and maintenance of business glossaries and data dictionaries.
• Participate in architecture and model design reviews to ensure proposed solutions comply with enterprise architecture and governance standards.

Metadata, Lineage & Documentation

• Maintain comprehensive documentation for conceptual, logical, and physical data models.
• Document:
  • Business definitions
  • Data entities and attributes
  • Source-to-target relationships
  • Data lineage
  • Business rules
  • Data ownership
  • Transformation requirements
• Maintain model metadata using enterprise modeling and/or data-cataloging platforms.
• Ensure appropriate traceability between business requirements, source systems, data models, transformation logic, and downstream consumption.

Data Quality & Integrity

• Contribute to data-quality frameworks by identifying:
  • Mandatory and optional data elements
  • Valid relationships
  • Referential-integrity requirements
  • Data constraints
  • Validation rules
  • Business rules
• Work with Data Engineering and Data Governance teams to identify and resolve data-quality and consistency issues.
• Ensure models appropriately support auditability, historical tracking, lineage, and data traceability.
• Identify data redundancies, inconsistencies, and opportunities for standardization and reuse.

Required Skills & Experience

• 12+ years of experience in data management, data architecture, data warehousing, or related disciplines, with significant hands-on data-modeling experience.
• 7+ years of strong experience developing conceptual, logical, and physical data models.
• Deep understanding of:
  • Relational data modeling
  • Dimensional modeling
  • Star and snowflake schemas
  • Normalization and denormalization
  • Entity-relationship modeling
  • Data warehousing concepts
• Strong understanding of facts, dimensions, hierarchies, grain, surrogate keys, business keys, and Slowly Changing Dimensions.
• Strong experience defining entities, attributes, relationships, cardinality, primary/foreign keys, and referential-integrity rules.
• Experience translating complex business requirements into enterprise data structures and models.
• Strong knowledge of SQL and relational database concepts.
• Experience with modern cloud data platforms, data lakes, data warehouses, or lakehouse architectures.
• Understanding of ETL/ELT processes and data-integration patterns.
• Experience with metadata management, data lineage, data dictionaries, and business glossaries.
• Good understanding of Master Data Management (MDM), reference data, data quality, and data governance.
• Experience working with enterprise architecture, Data Engineering, analytics, and application-development teams.

Data Modeling Tools

Hands-on experience with one or more enterprise data-modeling tools such as:

• ER/Studio
• ERwin Data Modeler
• SAP PowerDesigner
• Visio
• Lucidchart
• Or equivalent enterprise modeling platforms

Cloud & Technology Experience

Experience with one or more modern data platforms is preferred, including:

• Snowflake
• Databricks
• Microsoft Azure / Azure Synapse
• AWS data services
• Google Cloud Platform / BigQuery
• SQL Server
• Oracle
• PostgreSQL or other enterprise relational databases

Exposure to modern architectures such as data lakehouse, data mesh, domain-oriented data architecture, and cloud-native analytics platforms is an advantage.

Preferred Qualifications

• Experience supporting large-scale enterprise data-transformation or modernization programs.
• Experience modeling data across multiple business domains and source systems.
• Experience working in complex environments involving both operational and analytical data.
• Knowledge of data cataloging and governance platforms such as Collibra, Alation, Microsoft Purview, or equivalent tools.
• Understanding of data privacy, security, retention, and regulatory requirements.
• Experience working in Healthcare, Life Sciences, Pharmaceutical, or other regulated industries is preferred.
• Familiarity with Agile delivery methodologies and enterprise architecture frameworks.

Key Competencies

• Strong analytical and problem-solving skills.
• Ability to understand complex business processes and translate them into structured data models.
• Strong attention to detail and commitment to data consistency and quality.
• Ability to communicate data concepts effectively to both technical and non-technical stakeholders.
• Strong collaboration skills across business, architecture, engineering, governance, and analytics teams.
• Ability to independently lead data-modeling discussions and design reviews

Key Skills

Education

Any Gradute