Data Modeler

NTT DATA's Client•Pittsburgh, PA

1d•Remote

About The Position

NTT DATA's client is seeking an experienced, hands-on Data Modeler to design, develop, and maintain scalable enterprise data models for analytics, reporting, operational applications, data warehousing, and data integration. The role collaborates with business stakeholders, Data Architects, Data Engineers, analysts, application teams, and governance teams to translate complex requirements into conceptual, logical, and physical models. The Data Modeler establishes consistent definitions, relationships, standards, and modeling practices across business domains and technology platforms. The position covers enterprise and dimensional modeling, data warehousing and analytics, standards and governance, metadata and lineage documentation, and data quality and integrity. The modeler documents business definitions, entities, attributes, source-to-target relationships, lineage, business rules, ownership, and transformation requirements, while supporting traceability from requirements and source systems through downstream data use. The ideal candidate has substantial enterprise modeling experience and can explain modeling choices and demonstrate work across relational and dimensional models, data warehousing, slowly changing dimensions, metadata, lineage, governance, and modern cloud data architectures. NTT DATA Services provides IT and business services and states that the role is remote. The posting describes an hourly compensation range using placeholders ($x - x/hourly), so no specific amount is provided. Benefits depend on the nature of the role offered and may include medical, dental, and vision insurance; flexible spending or health savings accounts; life and AD&D insurance; short- and long-term disability coverage; paid time off; employee assistance; 401(k) with company match; and additional voluntary or legally required benefits.

Requirements

12+ years of experience in data management, data architecture, data warehousing, or related disciplines, with significant hands-on data-modeling experience.
7+ years of experience developing conceptual, logical, and physical data models.
Deep understanding of relational modeling, dimensional modeling, star and snowflake schemas, normalization and denormalization, entity-relationship modeling, and data warehousing.
Strong understanding of facts, dimensions, hierarchies, grain, surrogate keys, business keys, and Slowly Changing Dimensions.
Experience defining entities, attributes, relationships, cardinality, primary and 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, reference data, data quality, and data governance.
Experience collaborating with enterprise architecture, Data Engineering, analytics, and application-development teams.
Hands-on experience with one or more enterprise data-modeling tools, such as ER/Studio, ERwin Data Modeler, SAP PowerDesigner, Visio, Lucidchart, or equivalent platforms.
Strong analytical and problem-solving skills; ability to understand complex business processes and translate them into structured models.
Strong attention to detail and commitment to data consistency and quality.
Ability to communicate data concepts effectively to technical and non-technical stakeholders and collaborate across business and technical teams.

Nice To Haves

Experience with Snowflake, Databricks, Microsoft Azure or Azure Synapse, AWS data services, Google Cloud Platform or BigQuery, SQL Server, Oracle, PostgreSQL, or other enterprise relational databases.
Exposure to data lakehouse, data mesh, domain-oriented data architecture, or cloud-native analytics platforms.
Experience supporting large-scale enterprise data-transformation or modernization programs.
Experience modeling data across multiple business domains and source systems.
Experience 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 in Healthcare, Life Sciences, Pharmaceutical, or other regulated industries.
Familiarity with Agile delivery methodologies and enterprise architecture frameworks.
Ability to provide concrete examples of enterprise data models and explain approaches to relational versus dimensional modeling.

Responsibilities

Design, develop, and maintain conceptual, logical, and physical data models for enterprise data platforms, analytics solutions, and business applications.
Translate business requirements, processes, and data requirements into scalable, reusable data structures.
Develop and maintain relational, dimensional, star-schema, snowflake-schema, and domain-oriented data models.
Define entities, attributes, relationships, keys, hierarchies, and reference-data structures.
Design models for transactional, analytical, historical, and reporting needs; normalize or denormalize them as appropriate.
Design dimensional models for data warehouses, data marts, and analytics platforms; define facts, dimensions, measures, hierarchies, and grain.
Design approaches for maintaining historical information, including Slowly Changing Dimensions.
Ensure models support reporting, BI, advanced analytics, and downstream consumption; partner with Data Engineers on ETL/ELT and data-processing implementation.
Define and support enterprise modeling standards, naming conventions, data types, and best practices.
Work with stakeholders to establish consistent definitions for critical business data and identify authoritative sources.
Support master data, reference data, metadata management, and data-governance initiatives; contribute to business glossaries and data dictionaries.
Participate in architecture and model-design reviews to ensure alignment with enterprise architecture and governance standards.
Maintain documentation for models, business definitions, entities, attributes, source-to-target relationships, lineage, business rules, data ownership, and transformation requirements.
Maintain model metadata in enterprise modeling or data-cataloging platforms and ensure traceability from requirements and source systems to models, transformations, and downstream consumption.
Contribute to data-quality frameworks by identifying required and optional elements, valid relationships, referential-integrity requirements, constraints, validation rules, and business rules.
Work with Data Engineering and Data Governance teams to identify and resolve data-quality and consistency issues.
Ensure models support auditability, historical tracking, lineage, and traceability; identify redundancies, inconsistencies, and opportunities for standardization and reuse.
Lead data-modeling discussions and design reviews independently.

Benefits

Medical, dental, and vision insurance may be available, depending on the nature of the role offered.
Flexible spending or health savings account may be available, depending on the nature of the role offered.
Life and AD&D insurance may be available, depending on the nature of the role offered.
Short- and long-term disability coverage may be available, depending on the nature of the role offered.
Paid time off may be available, depending on the nature of the role offered.
Employee assistance may be available, depending on the nature of the role offered.
Participation in a 401(k) program with company match may be available, depending on the nature of the role offered.
Additional voluntary or legally required benefits may be available.

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