Job description

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. What You’ll Do 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. DATA PLANE ARCHITECTURE • 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. TECHNICAL POC LEADERSHIP • 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. CROSS-FUNCTIONAL TECHNICAL LEADERSHIP • 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. Who You’ll Work With 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. What Makes You a Qualified Candidate 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. POSTGRES & DATABASE INTERNALS • 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. ARCHITECTURE & DELIVERY • 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 & AGENTIC SYSTEMS • 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. What You’ll Bring • 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. Why We Think You'll Love Teradata 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. #LI-MN1

Requirements

Must-haves

What You’ll Do
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.
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.
Who You’ll Work With
What Makes You a Qualified Candidate
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.
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.
What You’ll Bring
Why We Think You'll Love Teradata

Nice-to-haves

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aipostgresapirestgopythonllmsql

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