Engineering Manager

We're hiring an Engineering Manager to own a backend pod inside Fulfillment or Marketplace - the engines that automate the service lifecycle for commercial facilities maintenance across thousands of customers and a large provider network. Your team's work ladders into DMG's north stars: Zero Touch Revenue and 80% machine assignment. This is a high-ownership role at an aggressively AI-first engineering org. You'll inherit a pod of 6-9 engineers, set the bar for how they build, and lead the shift to AI-native engineering.

Responsibilities:

• Delivery and execution. Sprint health, scope discipline, and predictable delivery. You read a sprint like an operator and intervene early and specifically.
• Engineering excellence. The quality bar: code review, test coverage, observability, on-call health, and incident response. You review your team's PRs yourself.
• Technical leadership. You drive design and review it on the merits - database choices, idempotency, rate limits - across a Java- and Python-based stack with Kafka, MongoDB, PostgreSQL, Temporal, and Snowflake.
• AI leverage. You use AI as a core operating practice to multiply your team's output, and you raise the team's AI bar in development, review, and instrumentation.
• Product partnership. You partner with Product as a peer - cross-questioning a brief, asking what's driving a metric and what to instrument before building.
• People leadership. 1:1s, growth, performance, and hiring. You read between the lines, name root causes, and treat engineers with dignity.

Requirements:

• 8-10 years of engineering experience, with 2+ years managing engineers as an EM / Sr. EM or equivalent.
• Hands-on depth in Java (primary) and/or Python, with distributed systems (Kafka, MongoDB, PostgreSQL, Redis, and Temporal) and cloud (AWS, Kubernetes).
• A current, demonstrable AI-assisted development practice.
• Excellent written and verbal communication.
• A track record of hiring and growing engineers.

Nice-to-haves:

• Experience scaling or standing up a team.
• High-performance product, startup, or enterprise-software background.
• Familiarity with ML/AI-in-production patterns (MLOps, LLM observability, agentic systems).

What we look for:

• Presence and conviction. You're engaged and opinionated in cross-functional discussions, take clear positions, and back them with reasoning.
• Decisiveness under ambiguity. You build a mental model from imperfect information, make the call, and own it.
• Genuine AI fluency. You have a point of view on how AI changes the way a team operates, and you've put it into practice.
• Strong design judgment. You can review a system design and articulate what to improve.