Manager, Software Engineering

You'll lead the AI Platform Team, the engine room for agentic workflows at dbt Labs. This team doesn't just build features; it builds the Agentic Platform that powers both our internal AI features and external AI integrations. In this role, you can expect to: Build, lead, and coach a team of 5–8 engineers focused on building a robust, scalable platform for AI agents. Architect the "Agent-First" Experience: Move dbt beyond a UI-driven tool by building the APIs and services required for agents to reason, plan, and execute within the dbt ecosystem. Define dbt’s MCP Strategy: Lead the development of dbt MCP tools that allow Claude, Codex, and other LLMs to fetch context, validate SQL, and understand metrics without leaving their development environment. Bridge Platform and Product: Partner with product teams to ensure that the AI Platform provides the necessary primitives (memory, tool-calling, and reasoning loops) for dbt’s specific agentic use cases. Coach engineers in building "Agent-ready" codebases—focusing on deterministic outputs from a non-deterministic world and the nuances of tool-use optimization. Drive Technical Excellence: Establish the standards for how agents should interact with dbt Cloud, ensuring security, governance, and auditability are never compromised for autonomy. Mandatory Skills 3+ years in people management leading high-performing software engineering teams. Experience with Agentic Architectures: You understand the lifecycle of an agentic loop (Plan -> Act -> Observe) and how to build infrastructure that supports it. Technical Breadth in APIs & Protocols: Deep experience with API design, and ideally, familiarity with emerging standards like MCP (Model Context Protocol) or OpenAI Function Calling. Software Engineering Fundamentals: You have a strong POV on how to maintain dbt’s "Analytics Engineering" rigors (testing, CI/CD) in an AI-driven world. You are a good fit if you have: A passion for Developer Tools: You understand the workflow of a data engineer and how tools like Claude Code or Codex are changing that workflow. Experience with Orchestration: You’ve built systems that manage state and context for LLMs. Strategic Collaboration: You can partner with external AI labs and internal teams to ensure dbt is the preferred "data context" layer for all major LLMs. You’ll have an edge if you have: Direct experience building or contributing to MCP servers. Experience in the "Modern Data Stack": You understand the importance of the dbt Semantic Layer and how it acts as a "source of truth" for AI. Excellent written communication skills: Essential for a remote-first culture and for documenting the "rules of engagement" for AI agents.