Sr Mgr, Product Engineering
$146,200–$243,600 year
HybridChicago, Illinois, United States
Job Summary
Lead cross-functional, distributed teams of up to 20 engineers to oversee Supplier Gather and Core domains within the Product Information Management organization. Drive the building and support of unified shared components, integrate and deploy Machine Learning models into platform capabilities, and shape the roadmap in partnership with Product, Architecture, and Machine Learning. Manage and mentor engineers of all levels, foster a high-performing culture grounded in psychological safety, and champion DevOps, CI/CD, and cloud-native best practices. Strengthen agile practices, observability, and operational rigor while using metrics to assess system health and delivery velocity. Report to the Director of Engineering, Product Information Management, and work on a hybrid schedule based in downtown Chicago (8 days per month onsite).
Required Qualifications
- 7+ years of work experience in a technical capacity
- 3 or more years of people management or team leadership
- A bachelor's degree in computer science, engineering, or related study or equivalent project-related experience
- Ability to weigh multiple facets to make strong decisions in an enterprise environment with a diverse set of stakeholders
- Strong written and verbal communication skills, including the ability to conduct effective performance conversations
- Exceptional interpersonal skills ensuring effective collaboration with team members, stakeholders, and vendors
- Experience working with onshore/offshore contractors and managing vendors
- Must be authorized to work in the United States now and for the foreseeable future
Desired Qualifications
- Master's degree preferred
- You bring a track record of putting AI and machine learning to work inside your teams, championing agentic development tools, AI-assisted coding, and automation that lift delivery velocity and engineering quality
- You have partnered closely with Machine Learning teams to integrate and deploy models into platform capabilities, turning ML potential into shipped, measurable business impact
- You lead engineers through AI adoption with a people-first approach, building a culture of continuous learning that turns emerging AI capabilities into practical, scalable team practices
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