Architect, Agentic Databases

• 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 AgentStack, 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 codebases. You are comfortable reading Postgres source to understand undocumented behavior, trace a performance regression, or evaluate how a patch will interact with the rest of the system.
• Systems programming proficiency in C or C++ for data plane work; familiarity with higher-level languages (Go, Python, Ruby) for control plane and tooling work.
• Strong understanding of storage engine design trade-offs: B-tree vs. LSM, page-based vs. log-structured storage, write amplification, read amplification, space amplification, and how each affects OLTP workload patterns.

• 12+ years of industry experience designing, building, and operating large-scale distributed systems, databases, or cloud data infrastructure.
• Demonstrated ability to own an architecture end-to-end: from initial design through technical PoC, production launch, and post-launch iteration — with measurable outcomes at each stage.
• Experience building or evaluating high-availability, multi-tenant database systems in cloud environments, including the control plane concerns: automated failover, backup and restore, upgrade automation, and multi-tenant isolation.
• Comfortable functioning as both a problem solver and a problem finder: you shape the technical roadmap and identify what is not on it yet.
• Strong written and verbal communication — you can write a technical design document that engineers trust and an executive summary that leaders act on.
• BS/MS in Computer Science or a related field, or equivalent depth demonstrated through production work.

• AI-native working style: you use AI as a high-trust collaborator in your own engineering work, prototype faster because of it, and look for ways to make the systems you build more useful to AI agents.
• Practical understanding of agentic workload patterns: what makes them different from interactive OLTP (bursty parallelism, LLM-generated SQL, stateful session continuity, branch-on-demand isolation) and what database design decisions those patterns require.

• Familiarity with the Model Context Protocol (MCP) and major agent frameworks (LangChain, LangGraph, OpenAI Agents SDK) at the level needed to define the integration surface between the Agentic Database and the agent execution layer.

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.

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Pay Rate: $182,700.00 - $228,400.00 - $274,000.00 Annually