Senior Staff Engineer, Agentic Databases

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.

You will own the data plane architecture — the Postgres engine, storage, query execution, WAL handling, extensions— and you will drive the Technical PoC that proves the design against live customer workloads.

• Define the end-to-end technical architecture of the Agentic Database data plane:
  Postgres engine configuration, storage model, WAL strategy, MVCC handling, extension framework, and the five Agent Services layered on top.
• Make the foundational architectural calls on shared-storage vs. local-storage, separation of compute and storage, branching and copy-on-write semantics, and how Postgres extensions interact with the custom storage layer.
• Own the technical trade-off decisions — consistency vs. availability, performance vs. cost, scaling limits, durability guarantees — and document them clearly enough that engineering teams can execute without re-litigating them.
• Design the integration architecture between the Agentic Database and Teradata Fabric, Teradata Context Engine, and the Enterprise MCP/Agent Stack platform, including CDC pipelines from the operational layer to Teradata’s OLAP engine.

• Own the Technical PoC end-to-end: design the test harness, select the representative customer workloads, define the success criteria, run the benchmarks, and produce the written findings that either validate the architecture or trigger a course correction.

• Serve as the technical authority in architecture reviews, design discussions, and cross-team dependency resolution — breaking down complex problems and building consensus across engineering, product, and infrastructure teams.
• Review and approve technical designs from engineering leads; push back on architectures that trade long-term maintainability for short-term velocity.
• Mentor senior and staff engineers on database internals, distributed systems design, and production-quality engineering standards.

You will work closely with senior engineering leaders, database kernel and storage engineers, product management, cloud infrastructure teams, and architecture partners across Teradata. This role will be a technical bridge between the core Agentic Database team and adjacent platform teams, including Teradata Fabric, Context Engine, Enterprise MCP, and Agent Stack, ensuring the data plane is designed as part of a broader AI-enabled enterprise architecture rather than as an isolated database component.

You will also partner with customer-facing technical teams and selected design partners to validate the architecture against real-world workloads, performance expectations, and operational requirements. Success in this role will depend on building trust across deeply technical stakeholders, aligning teams around clear trade-offs, and helping turn early architectural decisions into a scalable, production-ready foundation for agentic workloads.

A low-ego, experimental mindset who treats AI as a high-trust collaborator in their own work, navigates large complex codebases comfortably, and moves from architecture to working prototype without losing momentum. You are a problem finder as much as a problem solver — you shape the technical roadmap, not just execute it.

• Deep, hands-on Postgres expertise: you have worked at the source level, not just as an operator. You understand the query planner and optimizer (cost models, statistics, join ordering, partial indexes), the storage layer (heap, FSM, visibility map, TOAST), WAL internals, MVCC, vacuum, and the extension API.
• Experience navigating and modifying large C…