Manager, Software Engineering

dbt Labs is the pioneer of analytics engineering, helping data teams transform raw data into reliable, actionable insights. They are seeking a Manager of Software Engineering to lead the AI Platform Team, focusing on building a robust platform for AI agents and ensuring technical excellence in their interactions with dbt Cloud.

Responsibilities:

• 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

Requirements:

• 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
• 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