Note: The job is a remote job and is open to candidates in USA. Teradata provides an Autonomous Knowledge Platform that unifies data, knowledge, and business context to deliver enterprise intelligence and AI value. The Architect, Agentic Databases will own the Agentic Database data plane architecture, lead an end-to-end technical proof of concept, define integrations with adjacent Teradata platforms, and provide cross-functional technical leadership for scalable agentic workloads.

Responsibilities

• 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/AgentStack 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

Skills

• 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

Benefits

• Flexible work model
• People-first culture
• Focus on employee well-being
• Inclusive environment

Company Overview

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