Senior Data Modeler

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.

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.

• 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.

• Model data for modern cloud data platforms, data lakes, lake houses, 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.

• 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.

• 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…