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Teradata is building the missing data layer for production AI agents — and this role leads that effort. As Senior Director of Agentic Database Engineering, you will own the technical architecture and delivery of Teradata’s Agentic Database: a converged operational database purpose-built to power enterprise-grade AI agents at scale.

You will translate a well-defined product strategy into shipping software, leading an engineering organization responsible for five production‑critical Agent Services: Semantic Context Layer, Agent Memory, LLM Cache, Agent Tracer, and Elastic/Ephemeral Compute. You will work directly with the PM and Engg leadership teams to execute a build/OEM/acquire decision and deliver a 2026 early‑access launch.

This is a defining infrastructure role at the intersection of Postgres, vector retrieval, agentic AI, and governed enterprise data — inside a company whose decades of trusted enterprise context is an asset no competitor can quickly replicate.

McKinsey reports that fewer than 10% of enterprises have scaled AI agents to tangible value, with 80% citing data limitations as the primary barrier. Traditional architectures were not designed for the semantic context, durable state, semantic caching, and runtime traceability that production agents require. This role closes that gap and helps build an engine for the same.

You will architect and deliver a foundational data engine layer that forms the Teradata Agentic Database capabilities:

• Agent Memory — durable short- and long‑term memory for conversations, session state, checkpoints, and shared context, enabling reliable multi‑agent coordination and warm‑start recovery.
• LLM Cache — semantic similarity caching that eliminates redundant LLM calls on repeated agent queries, reducing token costs and response latency at scale.
• Agent Tracer — end‑to‑end observability across every prompt, tool call, and hand‑off, with a lineage graph that makes agentic decision‑making explainable and auditable.

• Define and own the end‑to‑end technical architecture of the Teradata Agentic Database, establishing Postgres as the operational foundation with pgvector, HNSW, full‑text search, JSONB, MVCC, CDC pipelines, and serverless branching.
• Design and validate a reference architecture against live customer workloads.

• Recruit, hire, and develop a high‑performing senior engineering organization specializing in serverless databases, vector retrieval, agent frameworks, and distributed systems.
• Set engineering culture: high technical bar, production‑first discipline, clear velocity targets, and tight product‑engineering partnership.
• Lead engineering managers and individual contributors across multiple concurrent workstreams on an aggressive 2026 launch timeline.

• Partner with Product Management to translate the Agentic Database PRD into a phased engineering roadmap with milestones.
• Drive engineering decisions informed by the three core personas: Agent Developer, Data Architect, and Admin — prioritizing concurrency, governed access, production reliability, and observability.
• Own architecture choices for agentic workload patterns: bursty parallelism, branch‑on‑demand isolation, LLM‑generated SQL safety, stateful session continuity, and warm‑start performance.

• Integrate the Agentic Database with Teradata Fabric, Teradata Context Engine, and the Enterprise MCP/AgentStack platform, enabling seamless analytical and operational query capabilities on a single governed platform.
• Build CDC pipelines between the operational Postgres layer and Teradata’s OLAP analytics engine for unified query coverage.
• Ensure compatibility with leading agentic frameworks — LangChain, LangGraph, OpenAI Agents SDK, AutoGen — and the MCP tooling ecosystem.

• Deep expertise in relational database internals: query optimizer design (cost‑based planning, statistics, cardinality estimation, join ordering), storage engines, buffer pool management, and transaction processing.
• Hands‑on experience building or extending a production OLTP database engine — Postgres, MySQL, or equivalent — including WAL, MVCC, lock management, and recovery subsystems.
• Proficiency with Postgres internals and extensions: pgvector, pg_trgm, custom access methods, index types (HNSW, IVFFlat, GIN, BRIN, GIST), and connection pooling (PgBouncer, Pgpool‑II).
• Experience with serverless database architectures, copy‑on‑write branching, and scale‑to‑zero compute — including familiarity with Neon, Supabase, PlanetScale, or equivalent platforms.
• Strong command of distributed systems fundamentals: consensus protocols, replication topologies, isolation levels, CDC, and exactly‑once semantics.

• 20+ years of engineering experience, including 10+ years leading senior engineering teams building and operating production data systems at enterprise scale.
• Track record shipping production database or data infrastructure products to enterprise customers under strict governance, compliance, and SLA requirements.
• Proven ability to recruit, retain, and grow senior‑level engineering talent in a competitive market.
• Executive‑level communication skills: able to distill complex architectural trade‑offs into clear board‑level narratives, written and verbal.

• Production experience with vector retrieval systems (pgvector, Pinecone, Weaviate, Qdrant) for RAG, semantic search, or LLM caching applications.
• Familiarity with agent orchestration frameworks — LangChain, LangGraph, OpenAI Agents SDK, AutoGen — and the Model Context Protocol (MCP) ecosystem.
• Practical understanding of agentic workload patterns: bursty parallelism, stateful session continuity, LLM‑generated SQL safety, and branch‑on‑demand isolation.

• Prior experience at a database startup or as a founding/senior engineer leader of a data infrastructure product.
• Background in enterprise data warehousing, OLAP systems, or hybrid OLTP/OLAP architectures.
• Bachelors, Masters, or PhD in Computer Science.
• Deep, expert knowledge of Postgres (WAL, extensions, configuration, replication, etc) and/or other OLTP (SQL/NoSQL) systems.
• Comfortable navigating large, complex codebases and leading cross‑team architecture efforts.
• A track record of driving projects from concept to production with measurable impact.
• Excellent communication skills and the ability to influence across engineering and product organizations.

Teradata is the cloud analytics and data platform company that powers the enterprise intelligence behind the world’s most demanding workloads. With decades of governed enterprise data, a True Hybrid Multi‑Cloud architecture spanning AWS, Azure, and GCP, and the industry’s deepest expertise in large‑scale analytical SQL, Teradata is uniquely positioned to become the production data infrastructure for enterprise AI agents. The Agentic Database initiative is a company‑defining investment — and this role sits at its center.