Our Company

At Teradata, we believe that people thrive when empowered with better information. That’s why we built the most complete cloud analytics and data platform for AI. By delivering harmonized data, trusted AI, and faster innovation, we uplift and empower our customers—and our customers’ customers—to make better, more confident decisions. The world’s top companies across every major industry trust Teradata to improve business performance, enrich customer experiences, and fully integrate data across the enterprise.

The Opportunity 

Enterprise AI has a context problem. Organizations are building AI agents and copilots that hit a wall the moment they try to reason about data they don't understand — they don't know what "revenue" means in your business, which tables are certified, or what access policies apply. The gap isn't the model. It's the metadata. 

Teradata serves 75% of the Fortune 100. As these customers deploy GenAI across lakehouse and open table format architectures, we need a Senior Staff PM to own the metadata and governance layer that both humans and AI agents depend on. 

You'll define the vision, strategy, and roadmap for how Teradata captures, structures, surfaces, and governs metadata — and how that metadata becomes the context foundation for AI-driven enterprise analytics. 

What You'll Do 

Vision & Strategy — Own the end-to-end product vision for metadata management, data governance, and AI context services across Teradata's core engine, Iceberg catalog integrations, and partner ecosystems. Drive the roadmap spanning cataloging, lineage, classification, access policies, compliance workflows, and AI-ready metadata services. 

AI-Native Governance — Define how Teradata's metadata platform serves as the governed context substrate for AI agents. Drive AI-powered governance: automated enrichment, intelligent classification, lineage inference, anomaly detection, and natural language policy authoring. Own the strategy for agent-facing interfaces (MCP protocol, REST/GraphQL APIs, semantic layer integrations) that let copilots and autonomous agents consume governed metadata at inference time. 

Execution — Translate ambiguous enterprise problems into structured plans. Partner with engineering to ship high-quality capabilities at scale. Own the full lifecycle from customer discovery through launch and adoption. 

Customer & Market — Build deep relationships with CDOs, AI/ML platform leads, and governance teams. Conduct competitive analysis across Databricks Unity Catalog, Snowflake Horizon/Cortex, Collibra, Alation, Atlan, and DataHub. Engage analysts (Gartner, Forrester) to shape Teradata's narrative at the intersection of governance and AI. 

Cross-Functional Leadership — Lead initiatives across engineering, field, marketing, and customer success. Develop GTM strategies, battle cards, and AI use-case demos. Influence without authority across a globally distributed org. 

What Makes You a Qualified Candidate:

• 10+ years in product management; 5+ years in data infrastructure, metadata, governance, or data platforms. 

• Deep knowledge of metadata concepts (technical, business, operational metadata; table/column-level lineage; cataloging; business glossaries) and how these map to AI readiness. 

• Strong understanding of governance frameworks, compliance regulations (GDPR, CCPA, HIPAA), and access control patterns (RBAC, ABAC, tag-based policies). 

• Familiarity with modern data architectures: warehouses, lakes, lakehouse patterns, open table formats (Apache Iceberg, Delta Lake), cloud platforms (AWS, Azure, GCP). 

• Understanding of how GenAI/LLMs interact with structured data — text-to-SQL, RAG, semantic layers, and metadata as context for AI reasoning. 

• Experience building for enterprise personas: data engineers, analysts, stewards, CDOs, AI/ML engineers, and compliance teams. 
• Excellent communication skills — crisp writing, no jargon walls. 

What You'll Bring:

• Hands-on knowledge of catalog/governance tools (Collibra, Alation, Atlan, DataHub, Unity Catalog, Snowflake Horizon, AWS Glue). 

• Familiarity with Iceberg internals — metadata structure, catalog implementations (REST, Hive, Polaris, Nessie). 

• Experience with AI agent architectures, context engineering, MCP protocol, or building products that serve metadata to AI systems. 

• Hands-on LLM application development — GenAI for data discovery, auto-documentation, intelligent search, or policy enforcement. 

What Sets You Apart 

• You see metadata as the context layer that powers discovery, trust, compliance, and AI — not a feature checklist. 

• You understand the "AI context gap" firsthand and have opinions on how to close it. 

• You are intellectually honest — you can articulate gaps as clearly as wins. 
• You move fast, prototype with AI tools, and don't wait for permission. 

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