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.

Experience
7-11 yrs