Senior Manager Context Architecture

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.

Help shape how enterprises turn data into trusted knowledge and context for analytics, AI and AI agents.

The Senior Manager, Knowledge & Context Architecture is a hands-on technical leadership role within Teradata's Worldwide Knowledge & Context Architecture team. You will lead architecture activities across the Americas, working with strategic customers, prospects and account teams to create practical, trusted foundations for AI.

The role combines strong foundations in data architecture, data modelling and data engineering with modern approaches to metadata, semantics, ontologies, knowledge representation, data products and agentic AI. You will work directly on important customer engagements while helping develop architecture capability across the region.

Our mission is to design the knowledge an enterprise runs on, engineer the context its AI agents need, and prove that context can be trusted. This helps make Teradata's vision for an Autonomous Knowledge Platform real for customers.

• Lead Knowledge & Context Architecture activities across the Americas, working directly with strategic customers, prospects and Teradata account teams.

• Act as a hands-on senior architect, leading discovery, workshops, design sessions and technical engagements.

• Help customers strengthen the data foundations needed for AI, including data models, metadata, semantics, lineage, quality, governance and reusable data products.

• Design architectures that bring together structured and unstructured data, metadata, business meaning and operational knowledge to provide trusted context for AI applications and agents.

• Apply data architecture and modelling skills to turn business concepts, relationships, definitions and rules into usable enterprise knowledge.

• Develop practical approaches for capturing and curating context, including semantic models, ontologies, industry models, metadata and AI-native data products.

• Help customers assess whether context supplied to AI systems is complete, accurate, current and trustworthy.

• Lead Context Engine and Knowledge Architecture workshops, deep dives and strategic proofs of concept with Americas customers.

• Turn patterns from customer work into reusable architecture assets, methods, accelerators and guidance that can be used globally.

• Work with the Americas field organisation to identify opportunities where Knowledge & Context Architecture can create customer value and support demand creation.

• Coach architects and technical practitioners across the Americas, helping build deeper skills in data, knowledge, context and AI architecture.

• Bring field evidence and customer feedback to Product Management and Engineering to help shape the Context Engine and broader platform direction.

You will be part of the Worldwide Knowledge & Context Architecture team and work closely with:

• Americas Customer Solution Architects, account teams and technical leaders

• Product Management and Engineering

• Office of the CTO

• AI Services and Consulting

• Context Engine Community of Excellence

• Global Knowledge & Context Architecture peers

We are looking for a strong data architect who can apply that foundation to knowledge, context and AI. You do not need to have worked under every title or with every technology listed below.

• Significant hands-on experience in data architecture, data modelling, data engineering or enterprise data management across complex environments.

• Practical knowledge of relational data architecture, including dimensional, normalised or domain-oriented modelling approaches.

• Strong practical SQL skills and confidence working directly with customer data when required.

• Experience with metadata management, data catalogues, lineage, data quality or governance.

• Experience designing modern data architectures spanning data warehouses, lake houses, object storage or cloud data platforms.

• Understanding of data-product concepts and how reusable, governed data assets support both human and AI consumers.

• A practical…