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Job ID #85536
Teradata
Senior Data Modeler
location_on
Jefferson City, MO
description
Summary
Our company
At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.
What You'll Do
We are seeking a highly experienced and hands-on Senior Data Modeler to lead the design and evolution of enterprise-scale data models that power analytics, AI, knowledge discovery, operational systems, and next-generation data and knowledge platforms across multiple domains.
This role is ideal for a technically deep practitioner who excels at translating complex business domains into scalable, high-quality conceptual, logical, and physical data models. The successful candidate will have extensive experience modeling large-scale enterprise data assets across structured, semi-structured, and graph-oriented environments while partnering closely with engineering, architecture, analytics, governance, and AI teams.
You will be responsible for establishing data modeling standards, driving enterprise-wide consistency, and ensuring that data is organized for scalability, interoperability, discoverability, governance, and AI readiness for Teradata Context Engine, our Autonomous Knowledge Platform. Context Engine is a platform that converts enterprise data estates into governed, agent-ready knowledge. Built for organizations where AI reliability and auditability are non-negotiable, Context Engine enables companies to deploy AI workflows on the data they already own - without rebuilding their infrastructure, without retraining their models, and without waiting for generic AI synthesis to learn the business terminology that took their analysts years to master.
_Responsibilities_
_Data Architecture & Modeling_
+ Design and maintain enterprise conceptual, logical, and physical data models across multiple business domains.
+ Create scalable data models supporting transactional systems, operational platforms, analytics, machine learning, and AI workloads.
+ Develop canonical data models, business information models, and semantic layers that enable consistent enterprise understanding of data.
+ Drive standardization of entities, relationships, taxonomies, hierarchies, and business definitions across the organization.
+ Define and enforce data modeling best practices, naming conventions, modeling patterns, and architecture standards.
_Enterprise Data Platform_
+ Model data for modern cloud data platforms, data lakes, lakehouses, warehouses, and real-time streaming architectures.
+ Design data structures optimized for performance, scalability, governance, and consumption.
+ Collaborate with data engineers to implement dimensional, normalized, denormalized, and domain-oriented data models.
+ Support data product development initiatives and data mesh architectures.
_AI, Analytics & Knowledge Systems_
+ Design models that support advanced analytics, AI, machine learning, RAG applications, semantic search, and knowledge management platforms.
+ Build and maintain business-friendly semantic representations that improve discoverability and reuse.
+ Contribute to enterprise ontology, taxonomy, and knowledge graph initiatives where applicable.
_Governance & Quality_
+ Partner with data governance teams to align models with data quality, stewardship, privacy, security, and compliance requirements.
+ Establish traceability between business definitions, metadata, source systems, and downstream data assets.
+ Ensure model consistency across operational, analytical, and AI ecosystems.
+ Support master data management (MDM) and reference data initiatives.
_Collaboration & Leadership_
+ Work directly with business stakeholders to understand complex processes and translate them into enterprise information models.
+ Mentor junior modelers, architects, and engineers on data modeling principles and best practices.
+ Lead architecture reviews and influence long-term data strategy.
+ Drive cross-functional alignment across product, engineering, analytics, governance, and architecture organizations.
Who You'll Work With
Partner with AI and data science teams to model features, training datasets, metadata, lineage, and business context.
What Makes You a Qualified Candidate
_Required Qualifications_
+ Bachelor's or Master's degree in Computer Science, Information Systems, Data Management, Mathematics, Engineering, or related field.
+ 8+ years of experience in data modeling, information architecture, or enterprise data architecture.
+ Demonstrated hands-on expertise creating conceptual, logical, and physical data models for large-scale enterprise environments.
+ Deep understanding of relational modeling theory, normalization, dimensional modeling, and analytical schema design.
+ Experience modeling enterprise-wide business domains and integrating data across complex systems.
+ Strong SQL and database design expertise.
+ Experience working with cloud-based data platforms and modern data architectures.
+ Proven ability to bridge business and technical stakeholders.
_Preferred Qualifications_
+ Experience with cloud ecosystems such as Azure, AWS, or Google Cloud.
+ Experience with Databricks, Snowflake, Synapse, BigQuery, Redshift, or similar platforms.
+ Knowledge of Data Vault, Kimball, Inmon, data mesh, and domain-driven design methodologies.
+ Experience with metadata management, data catalogs, and lineage platforms.
+ Exposure to graph databases, semantic technologies, ontologies, RDF, or knowledge graphs.
+ Understanding of AI/ML data requirements and semantic modeling for generative AI applications.
+ Experience supporting enterprise governance and master data management programs.
_Technical Skills_
_Data Modeling & Architecture_
+ Conceptual, Logical, and Physical Data Modeling
+ Enterprise Information Modeling
+ Dimensional Modeling (Star/Snowflake)
+ Data Vault Modeling
+ Master & Reference Data Modeling
+ Canonical Data Models
+ Domain-Driven Design
+ Semantic Data Modeling
_Data Platforms_
+ Azure Data Platform
+ Snowflake
+ Databricks
+ Synapse Analytics
+ AWS Data Services
+ Google Cloud Data Platform
_Databases_
+ SQL Server
+ PostgreSQL
+ Oracle
+ MySQL
+ MongoDB
+ Snowflake
+ Graph Databases (Neo4j, Neptune, Cosmos DB Graph)
_Tools_
+ Erwin
+ ER/Studio
+ SAP PowerDesigner
+ Lucidchart
+ Visio
+ Collibra
+ Alation
+ Informatica
+ Microsoft Purview
What You'll Bring
The ideal candidate combines:
+ Deep hands...
At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.
What You'll Do
We are seeking a highly experienced and hands-on Senior Data Modeler to lead the design and evolution of enterprise-scale data models that power analytics, AI, knowledge discovery, operational systems, and next-generation data and knowledge platforms across multiple domains.
This role is ideal for a technically deep practitioner who excels at translating complex business domains into scalable, high-quality conceptual, logical, and physical data models. The successful candidate will have extensive experience modeling large-scale enterprise data assets across structured, semi-structured, and graph-oriented environments while partnering closely with engineering, architecture, analytics, governance, and AI teams.
You will be responsible for establishing data modeling standards, driving enterprise-wide consistency, and ensuring that data is organized for scalability, interoperability, discoverability, governance, and AI readiness for Teradata Context Engine, our Autonomous Knowledge Platform. Context Engine is a platform that converts enterprise data estates into governed, agent-ready knowledge. Built for organizations where AI reliability and auditability are non-negotiable, Context Engine enables companies to deploy AI workflows on the data they already own - without rebuilding their infrastructure, without retraining their models, and without waiting for generic AI synthesis to learn the business terminology that took their analysts years to master.
_Responsibilities_
_Data Architecture & Modeling_
+ Design and maintain enterprise conceptual, logical, and physical data models across multiple business domains.
+ Create scalable data models supporting transactional systems, operational platforms, analytics, machine learning, and AI workloads.
+ Develop canonical data models, business information models, and semantic layers that enable consistent enterprise understanding of data.
+ Drive standardization of entities, relationships, taxonomies, hierarchies, and business definitions across the organization.
+ Define and enforce data modeling best practices, naming conventions, modeling patterns, and architecture standards.
_Enterprise Data Platform_
+ Model data for modern cloud data platforms, data lakes, lakehouses, warehouses, and real-time streaming architectures.
+ Design data structures optimized for performance, scalability, governance, and consumption.
+ Collaborate with data engineers to implement dimensional, normalized, denormalized, and domain-oriented data models.
+ Support data product development initiatives and data mesh architectures.
_AI, Analytics & Knowledge Systems_
+ Design models that support advanced analytics, AI, machine learning, RAG applications, semantic search, and knowledge management platforms.
+ Build and maintain business-friendly semantic representations that improve discoverability and reuse.
+ Contribute to enterprise ontology, taxonomy, and knowledge graph initiatives where applicable.
_Governance & Quality_
+ Partner with data governance teams to align models with data quality, stewardship, privacy, security, and compliance requirements.
+ Establish traceability between business definitions, metadata, source systems, and downstream data assets.
+ Ensure model consistency across operational, analytical, and AI ecosystems.
+ Support master data management (MDM) and reference data initiatives.
_Collaboration & Leadership_
+ Work directly with business stakeholders to understand complex processes and translate them into enterprise information models.
+ Mentor junior modelers, architects, and engineers on data modeling principles and best practices.
+ Lead architecture reviews and influence long-term data strategy.
+ Drive cross-functional alignment across product, engineering, analytics, governance, and architecture organizations.
Who You'll Work With
Partner with AI and data science teams to model features, training datasets, metadata, lineage, and business context.
What Makes You a Qualified Candidate
_Required Qualifications_
+ Bachelor's or Master's degree in Computer Science, Information Systems, Data Management, Mathematics, Engineering, or related field.
+ 8+ years of experience in data modeling, information architecture, or enterprise data architecture.
+ Demonstrated hands-on expertise creating conceptual, logical, and physical data models for large-scale enterprise environments.
+ Deep understanding of relational modeling theory, normalization, dimensional modeling, and analytical schema design.
+ Experience modeling enterprise-wide business domains and integrating data across complex systems.
+ Strong SQL and database design expertise.
+ Experience working with cloud-based data platforms and modern data architectures.
+ Proven ability to bridge business and technical stakeholders.
_Preferred Qualifications_
+ Experience with cloud ecosystems such as Azure, AWS, or Google Cloud.
+ Experience with Databricks, Snowflake, Synapse, BigQuery, Redshift, or similar platforms.
+ Knowledge of Data Vault, Kimball, Inmon, data mesh, and domain-driven design methodologies.
+ Experience with metadata management, data catalogs, and lineage platforms.
+ Exposure to graph databases, semantic technologies, ontologies, RDF, or knowledge graphs.
+ Understanding of AI/ML data requirements and semantic modeling for generative AI applications.
+ Experience supporting enterprise governance and master data management programs.
_Technical Skills_
_Data Modeling & Architecture_
+ Conceptual, Logical, and Physical Data Modeling
+ Enterprise Information Modeling
+ Dimensional Modeling (Star/Snowflake)
+ Data Vault Modeling
+ Master & Reference Data Modeling
+ Canonical Data Models
+ Domain-Driven Design
+ Semantic Data Modeling
_Data Platforms_
+ Azure Data Platform
+ Snowflake
+ Databricks
+ Synapse Analytics
+ AWS Data Services
+ Google Cloud Data Platform
_Databases_
+ SQL Server
+ PostgreSQL
+ Oracle
+ MySQL
+ MongoDB
+ Snowflake
+ Graph Databases (Neo4j, Neptune, Cosmos DB Graph)
_Tools_
+ Erwin
+ ER/Studio
+ SAP PowerDesigner
+ Lucidchart
+ Visio
+ Collibra
+ Alation
+ Informatica
+ Microsoft Purview
What You'll Bring
The ideal candidate combines:
+ Deep hands...
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