Job description

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 As a Senior Staff Product Manager, you will own the strategy, roadmap, and measurable outcomes for Teradata's Enterprise Context Engine. The Context Engine extracts, curates, and continuously improves metadata and business context, grounded in Teradata's industry knowledge models, to serve as a governed layer that accelerates human analysts and grounds AI agents in trusted enterprise meaning. You will connect technical depth with platform and ecosystem leadership across the full product lifecycle: customer discovery and knowledge acquisition, curation and governance, runtime delivery to people and agents, adoption, and continuous improvement. You will lead cross-functional prioritization across engineering, data science, developer relations, product marketing, and the field. Key Responsibilities Product Strategy, Roadmap & Outcomes • Own the end-to-end Context Engine roadmap, translating customer needs and business priorities into clear product requirements, prioritization decisions, and success criteria. • Drive the product lifecycle from discovery and prioritization through launch, adoption, and continuous improvement, with explicit accountability for customer value delivered. • Define the platform vision so that context capabilities are reusable across Teradata products, personas, and business domains, and across hybrid and multi-cloud environments. • Set the competitive position of the Context Engine against Databricks, Snowflake, Microsoft Fabric, and emerging AI data platform and semantic layer vendors. Knowledge Graph & Context Layer • Work across peers in design, architecture, and engineering to build a world-class approach for ingesting, harmonizing, and organizing both metadata and data from enterprise databases, data catalogs, BI tools, data pipelines, operational systems, enterprise tools, and unstructured knowledge sources into a unified, queryable knowledge graph. • Drive the strategy and requirements across Teradata’s context engine product including ontology management, semantic mapping and schema enrichment, entity resolution, conflict harmonization, and ai-assisted mapping of customer data assets to business concepts. • Lead the integration of Teradata's proprietary industry knowledge models, spanning financial services, healthcare, retail, manufacturing, telecommunications, and other regulated verticals, as first-class enrichment layers. • Establish the semantic interoperability posture for the Context Engine, including how it aligns with emerging semantic-layer and metadata standards and extends beyond BI metric definitions to process, policy, and decision context. Context Delivery for Humans and AI Agents • Own context delivery APIs and runtime retrieval capabilities, including semantic and hybrid search, graph-aware retrieval, and retrieval-augmented generation (RAG and GraphRAG) patterns. • Define how agents and agent frameworks discover, request, and consume context, including through Model Context Protocol (MCP) and other open agent interfaces. • Shape embedding and vector-indexing approaches and measure their effect on retrieval precision, agent reasoning efficiency, and downstream answer quality. Context Lifecycle, Quality & Trust • Define requirements for context lifecycle management: ingestion, curation, versioning, freshness guarantees, lineage, and deprecation of stale or conflicting knowledge. • Establish quality, coverage, and confidence-scoring frameworks so that context surfaced to humans and AI agents is accurate, explainable, and auditable. • Define governance, security, and policy-enforcement requirements appropriate for highly regulated enterprises. How Success is Measured • Customer adoption and production deployments of the Context Engine, including agent and application consumption of context. • Quality of contextual results: accuracy, coverage, confidence calibration, and improvement in agent task success and retrieval precision. • Platform usability as measured by targeted KPIs and customer sentiment • Pipeline influence, win-rate contribution, and analyst and customer perception of Teradata's context and trusted-AI position. Who You’ll Work With • Lead customer discovery with enterprise data, analytics, and AI leaders, and convert findings into differentiated product capabilities and reference solutions. • Partner with field teams, solution architects, and developer relations to validate context quality, deliver proof-of-value engagements, and accelerate adoption. • Work with product marketing to position Context Engine within Teradata's trusted AI narrative, and represent actively communicate that position to all levels of customers, analysts, and partners. What Makes You a Qualified Candidate • Bachelor's degree and 12+ years of experience, including 8+ years in product management or equivalent technical product leadership, with 4+ years on enterprise AI/ML, search or ML ranking, ontology or metadata curation platforms. • Deep familiarity and practical experience with knowledge graph technologies, metadata management, and semantic knowledge platforms, including ontology design, graph query languages (e.g., Gremlin, SPARQL, Cypher), and entity resolution/harmonization. • Strong technical fluency in enterprise data architecture: data catalogs, lineage, schema registries, metadata standards (e.g., OpenMetadata, Apache Atlas), and data governance frameworks. • Demonstrated experience building enterprise GraphRAG pipelines, hybrid semantic search, agent grounding, and domain-specific LLM applications. • Hands-on experience with agentic AI systems and a record of bringing agent-driven solutions from concept to production. • Demonstrable experience creating product requirements, including roadmaps, UX, API requirements, prioritization frameworks, and measurable success criteria in partnership with design and engineering. • Proven ability to lead and align engineering, data science, architecture, design, and go-to-market teams around clear product outcomes. What You’ll Bring • Foundational AI skills and the ability to understand how AI can be applied to improve outcomes in your own work tasks. Why We Think You'll Love Teradata We prioritize a people-first culture because we know our people are at the very heart of our success. We embrace a flexible work model because we trust our people to make decisions about how, when, and where they work. We focus on well-being because we care about our people and their ability to thrive both personally and professionally. We are committed to actively working to foster an inclusive environment that celebrates people for all of who they are. #LI-SK1

Requirements

Must-haves

Product Strategy, Roadmap & Outcomes
Own the end-to-end Context Engine roadmap, translating customer needs and business priorities into clear product requirements, prioritization decisions, and success criteria.
Drive the product lifecycle from discovery and prioritization through launch, adoption, and continuous improvement, with explicit accountability for customer value delivered.
Define the platform vision so that context capabilities are reusable across Teradata products, personas, and business domains, and across hybrid and multi-cloud environments.
Set the competitive position of the Context Engine against Databricks, Snowflake, Microsoft Fabric, and emerging AI data platform and semantic layer vendors.
Knowledge Graph & Context Layer
Context Delivery for Humans and AI Agents
Own context delivery APIs and runtime retrieval capabilities, including semantic and hybrid search, graph-aware retrieval, and retrieval-augmented generation (RAG and GraphRAG) patterns.
Define how agents and agent frameworks discover, request, and consume context, including through Model Context Protocol (MCP) and other open agent interfaces.
Shape embedding and vector-indexing approaches and measure their effect on retrieval precision, agent reasoning efficiency, and downstream answer quality.
Context Lifecycle, Quality & Trust
Define requirements for context lifecycle management: ingestion, curation, versioning, freshness guarantees, lineage, and deprecation of stale or conflicting knowledge.
Establish quality, coverage, and confidence-scoring frameworks so that context surfaced to humans and AI agents is accurate, explainable, and auditable.
Define governance, security, and policy-enforcement requirements appropriate for highly regulated enterprises.
How Success is Measured
Customer adoption and production deployments of the Context Engine, including agent and application consumption of context.
Quality of contextual results: accuracy, coverage, confidence calibration, and improvement in agent task success and retrieval precision.
Platform usability as measured by targeted KPIs and customer sentiment
Pipeline influence, win-rate contribution, and analyst and customer perception of Teradata's context and trusted-AI position.
Who You’ll Work With
Lead customer discovery with enterprise data, analytics, and AI leaders, and convert findings into differentiated product capabilities and reference solutions.
Partner with field teams, solution architects, and developer relations to validate context quality, deliver proof-of-value engagements, and accelerate adoption.
Work with product marketing to position Context Engine within Teradata's trusted AI narrative, and represent actively communicate that position to all levels of customers, analysts, and partners.
What Makes You a Qualified Candidate

Nice-to-haves

None flagged.

Application prep

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Benefits

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