Product Manager, Internal Tooling & Data
New
C
ComfrtE-commerce
United StatesFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 3+ years
- Required Skills
- AgileArtificial IntelligenceETLMachine LearningProduct ManagementSCRUMData engineeringRESTful APIsMicroservicesSaaS
Requirements
- 3+ years of Product Management experience, with a strong focus on SaaS, internal tools, platform products, data engineering, or backend systems.
- Deep technical fluency; comfortable navigating discussions around UI/UX, microservices, APIs, database architectures, and data pipelines (ETL/ELT).
- Proven experience driving automation initiatives and evaluating/implementing AI tools to solve operational bottlenecks.
- Exceptional analytical skills with the ability to translate complex business processes into streamlined technical workflows.
- Strong stakeholder management skills, capable of aligning engineering teams, operations, and leadership on technical roadmaps.
- Hands-on experience operating within Agile/Scrum environments.
Responsibilities
- Own the product vision and roadmap for internal tooling, automation initiatives, and back-office applications.
- Lead the company's AI adoption strategy, identifying opportunities to integrate LLMs, machine learning, and intelligent automations to streamline workflows.
- Manage the full lifecycle of multiple full-stack applications, balancing the needs of internal operational tools with external customer-facing services.
- Coordinate cross-functional efforts to ensure performance, scalability, and security across both internal and external application ecosystems.
- Define and manage the data engineering roadmap, partnering with data engineers to build robust pipelines, modern data warehousing solutions, and analytics infrastructure.
- Collaborate with engineering leadership to guide the transition toward a microservices architecture, ensuring internal services are scalable, decoupled, and highly performant.
- Write clear, technically sound Product Requirements Documents (PRDs), manage backlogs, and run agile sprints with backend and data engineering teams.
- Establish success metrics for internal products, focusing on operational efficiency, processing times, automation success rates, and data reliability.
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