Engineering Manager

Flooid•Cincinnati, OH

4d•$180,000•Hybrid

About The Position

The Engineering Manager is a people, delivery, and technical leadership role responsible for engineering effectiveness, capability, and the way software gets built across multiple teams within Flooid. This is a manager-of-managers role, not a single-team manager role. Reporting to the VP of Engineering, you will support the rules and standards we must operate by, while working with the VP to implement and evolve them as the organisation and technology change. Flooid builds the point-of-sale and unified commerce software that some of the world's largest retailers run their stores on. Agentic AI is already part of our engineering day, not a pilot but the direction we are committing to, and this role is critical in making it work at scale with the delivery discipline our customers depend on: high quality, getting faster over time, and hitting the dates we have committed.

Requirements

Proven experience line-managing Engineering Leads, team leads, or equivalent leaders across multiple teams, rather than a first-time people leader of a single team.
Strong people leadership skills, with experience line managing leaders, coaching for performance, and supporting career progression across multiple teams.
Deep technical understanding and credibility, with the ability to review, challenge, and support engineering decisions, architecture discussions, delivery trade-offs, and technical problem solving.
Fluency in Cursor, used as a normal part of how you lead rather than as an occasional demo, so you can review AI-assisted work with judgement and set the standard from first-hand use.
Practical experience of AI-assisted or agentic engineering in a working team, where you changed how software actually gets built.
Strong track record of delivering at high quality, improving speed over time, and hitting committed dates in complex product or customer-facing environments, with a clear view of modern Agile and DevOps practice.
Excellent stakeholder management.

Nice To Haves

Experience supporting a shift from bespoke delivery models to configurable, scalable products and platform-based ways of working.
Experience of spec-driven or script-driven development, and of agentic engineering frameworks such as Flow Next (flow-next.dev), used to define work clearly before agents implement it.
Experience driving engineering transformation or delivery maturity initiatives.
Comfortable balancing customer commitments, operational stability, technical quality, and time-to-market pressures across several teams.

Responsibilities

Line manage, coach, and develop Engineering Leads across multiple teams, creating clear expectations, strong leadership capability, and succession plans.
Own performance management and career development across your group: support and guide the teams, help people grow, intervene where they struggle and put clear performance improvement plans in place where needed.
Own delivery confidence across your teams, so work ships at high quality, with increasing speed, and to the times we have committed, driving cross-team planning, dependencies, and risk to improve forecasting and reduce escalation.
Partner closely with the Product, Architecture, and Professional Services on scope, sequencing, and trade-offs.
Work with the VP and other lead stakeholders on the agentic delivery lifecycle end to end, from specification through implementation, review, testing, and release, deciding where agents lead, where humans lead, and where a human signature is non-negotiable.
Be part of implementing that strategy: work hands-on in Cursor and Flow Next, and in the operating model alongside individual contributors and your Engineering Leads.
Make prompts, skills, and context first-class engineering artefacts that are versioned and reusable, and build evaluation into our quality system the way automated testing already is.
Work with Platform Engineering, Architecture, Cloud, and Security on the platform underneath: model access, observability, audit trails, and cost.
Responsibility for delivering agreed metrics and KPIs that make our improvements visible to the business, and use them actively to manage how we work: quality, flow, predictability, team health, and whether AI is genuinely helping.
Establish what good looks like in engineering process, Agile ways of working, and technical quality, and support our teams to grow career paths that make sense in an AI-assisted world.