Remote Product Manager, Data Modernization Platform

Mechanical Orchard, Inc.; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Mechanical Orchard, Inc. is seeking a Product Manager for Imogen Data to evolve data tooling from MVP to enterprise-scale.

You will work with engineers and cross-functional teams to turn legacy data into ready-for-production formats, enabling trusted modernization for large organizations worldwide. You’ll balance long-term strategy with fast-paced delivery, communicating clearly with stakeholders, and applying modern product thinking to complex, high-stakes data workloads.

Qualifications

• 4+ years in a product management role in a highly technical environment, preferably with data products
• Strong technical background with ability to grasp technical concepts and earn credibility with engineers
• Experience building products at enterprise-scale

Responsibilities

• Defining the product vision and strategy for your team
• Collaborating with forward-deployed engineering teams and technical partners to understand the product experience
• Identifying and prioritizing product investments based on user needs and business goals
• Partnering with engineering and technical leadership to define the team’s roadmap
• Driving execution of the team’s work with technical leadership
• Representing the product to executive stakeholders, partners and customers
• Articulating customer value and positioning within an evolving market landscape

Skills

PM experience

Technical background

Enterprise-scale

Data products

Education

Bachelor's or Master's in technical field

Job description

Mechanical Orchard, Inc. is seeking a Product Manager for Imogen Data to evolve data tooling from MVP to enterprise-scale.

You will work with engineers and cross-functional teams to turn legacy data into ready-for-production formats, enabling trusted modernization for large organizations worldwide. You’ll balance long-term strategy with fast-paced delivery, communicating clearly with stakeholders, and applying modern product thinking to complex, high-stakes data workloads.