Data Modeler (Level III) 2802

Required Skills

Erwin Data Modeler, IBM InfoSphere Data Architect, SAP PowerDesigner, or ER/Studio, PostgreSQL, SQL Server, Oracle, and MySQL, Snowflake, Databricks, Azure Synapse Analytics, Google BigQuery, or Amazon Redshift

Job Description

Key Responsibilities / Essential Duties

•Design and develop conceptual, logical, and physical data models for enterprise data warehouses, data lakes, and operational databases, ensuring alignment with organizational data architecture standards and pharmaceutical distribution data requirements.

•Build and maintain dimensional data models — including star schemas, snowflake schemas, and data vault architectures — to support advanced analytics, business intelligence reporting, and AI-driven decision-making across the organization.

•Define and enforce data modeling standards, naming conventions, design patterns, and best practices to ensure consistency, reusability, and scalability across all data modeling deliverables and delivery teams.

•Implement data governance and metadata management processes within data models, including data lineage tracking, data classification, business glossary alignment, and compliance with applicable regulatory and data protection requirements.

•Collaborate with data engineers, data architects, business analysts, and application development teams to gather and interpret complex business requirements, translating them into well-structured, normalized or denormalized data models as appropriate.

•Optimize data model performance through indexing strategies, partitioning schemes, query optimization, and denormalization techniques to maximize data retrieval efficiency and minimize processing costs across relational and cloud-based platforms.

•Architect scalable data models using cloud-native data platforms such as Snowflake, Databricks, Azure Synapse Analytics, or Google BigQuery, ensuring alignment with enterprise cloud migration and modernization strategies.

•Maintain version control and change management processes for all data modeling artifacts, including entity-relationship diagrams, data dictionaries, source-to-target mappings, and transformation specifications.

•Ensure data quality and integrity across all modeled data structures by implementing validation rules, referential integrity constraints, and data quality checks aligned with enterprise data governance frameworks.

•Evaluate emerging data modeling methodologies, tools, and technologies to identify opportunities for innovation and continuous improvement in enterprise data architecture practices.

•Communicate data modeling decisions, design rationale, and technical recommendations to cross-functional partners and stakeholders across multiple time zones, adapting communication approaches for diverse technical and non-technical audiences.

•Document data model designs, schema specifications, data dictionaries, and knowledge artifacts to ensure operational continuity during transitions, enabling seamless integration of evolving team structures.

Qualifications

Education

•Bachelor’s degree in Computer Science, Information Systems, Data Science, Mathematics, or a related technical field (required).

•Master’s degree in Computer Science, Data Science, Information Systems, or a related discipline (preferred).

Experience

•Minimum professional experience as per the Level in data modeling, data architecture, or a related data management discipline, with a focus on designing enterprise-scale conceptual, logical, and physical data models.

•Minimum of hands-on experience as per the Level building and maintaining dimensional data models, entity-relationship diagrams, and data schemas in production environments using industry-standard data modeling tools.

•Demonstrated experience with end-to-end data modeling workflows — including requirements gathering, conceptual design, logical modeling, physical implementation, and ongoing model governance — within a cloud-based enterprise environment.

•Experience in a regulated industry (pharmaceutical, healthcare, or life sciences) is a plus.

Certifications (Required / Preferred)

•Certified Data Management Professional (CDMP) or Microsoft Certified: Azure Data Engineer Associate (DP-203) — Required (one of the two).

•CDMP Certified Data Management Professional at Practitioner or Master level — Preferred.

•Oracle Database SQL Certified Associate or Snowflake SnowPro Core Certification — Preferred.

•The Open Group Architecture Framework (TOGAF) Certification — Preferred.

Knowledge, Skills & Abilities

•Expert-level proficiency in data modeling methodologies including conceptual, logical, and physical data modeling, with deep understanding of normalization, denormalization, dimensional modeling (star schema, snowflake schema), and data vault architecture for enterprise data warehouses and data lakes.

•Strong experience with industry-standard data modeling tools such as Erwin Data Modeler, IBM InfoSphere Data Architect, SAP PowerDesigner, or ER/Studio for designing, documenting, and managing enterprise data models across relational and cloud-based platforms.

•Proficiency in Structured Query Language (SQL) for complex query development, database schema creation, data definition language (DDL) operations, and performance optimization across relational database management systems including PostgreSQL, SQL Server, Oracle, and MySQL.

•Hands-on experience with cloud-native data platforms such as Snowflake, Databricks, Azure Synapse Analytics, Google BigQuery, or Amazon Redshift for designing and implementing scalable data models that support enterprise analytics and reporting requirements.

•Solid understanding of data governance, metadata management, and data quality frameworks, with the ability to implement data lineage tracking, business glossary alignment, data classification, and referential integrity constraints across all modeled data structures.

•Proficiency in data integration and ETL/ELT patterns, with understanding of how data models interact with data pipeline architectures built using tools such as Azure Data Factory, Informatica, or dbt to ensure seamless data flow from source systems to analytics platforms.

•Strong experience with version control and change management practices for data modeling artifacts, including entity-relationship diagrams, data dictionaries, source-to-target mappings, and transformation specifications.

•Experience with business intelligence and analytics platforms such as Microsoft Power BI, Tableau, or Looker, with the ability to design data models that optimize reporting performance and enable self-service analytics for business users.

•Strong analytical and problem-solving abilities with a detail-oriented mindset, commitment to data accuracy, and ability to evaluate trade-offs between normalization, performance, and usability when designing data structures for diverse business use cases.

•Excellent communication skills with the ability to articulate complex data modeling concepts, design rationale, and technical recommendations to diverse technical and non-technical stakeholders, and to mentor cross-functional team members on data modeling best practices.

Competencies

•Analytical Rigor & Precision: Applies systematic, detail-oriented approaches to data model design, ensuring every entity, attribute, relationship, and constraint is accurately defined and aligned with business requirements and enterprise data architecture standards.

•Technical Excellence: Maintains exceptionally high standards for data model quality, schema design consistency, and adherence to data modeling best practices across conceptual, logical, and physical modeling deliverables.

•Collaboration & Teamwork: Works effectively with cross-functional teams including data engineers, data architects, business analysts, and application development teams to translate complex business requirements into well-structured, scalable data models.

•Adaptability: Thrives in a fast-paced environment where data technologies, cloud platforms, and business requirements evolve rapidly, adjusting modeling approaches to accommodate new data sources, schema patterns, and enterprise modernization initiatives.

•Data Governance & Stewardship: Champions data quality, integrity, and compliance by proactively implementing governance frameworks, metadata standards, lineage tracking, and validation rules across all data modeling deliverables.

•Results Orientation: Focuses on delivering production-ready, scalable data models that generate tangible business value, enabling reliable analytics capabilities, optimized reporting performance, and measurable improvements in data accessibility across the organization.