Software Engineering Manager, dbt Labs

• 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
• 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

Desirable

• 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
• 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

• Interview with a Talent Acquisition Partner (30 Mins)
• Technical Interview with Hiring Manager (60 Mins)
• Team Interviews ( 3 rounds, 45 Mins each)
• Final Leadership Interview (30 Mins)