Note: The job is a remote job and is open to candidates in USA. Teradata is hiring a Senior Staff Product Manager to own the strategy, roadmap, and outcomes for its Enterprise Context Engine. The role leads development of a governed context layer that helps human analysts and AI agents use trusted enterprise meaning, from knowledge curation and governance through runtime delivery and adoption.
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
- 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
Skills
- 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
- Foundational AI skills and the ability to understand how AI can be applied to improve outcomes in your own work tasks
Benefits
- A flexible work model, with the flexibility to decide how, when, and where you work.
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