Senior Product Manager, Context Engine

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

• 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.

• 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.

• 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.

• 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.

• 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…