Data Product Owner: Insurance Data & Analytics Platform

Munich Re Specialty - North America; Hartford (CT); Hybrid; USD 189,000 - 232,000; Full time

Benefits offered by this job

Annual bonus

Health insurance

401k match

PTO

Job summary

Munich Reinsurance America, Inc. seeks a Data Product Owner to lead data product work within the Finance domain for Data Marketplace initiatives.

You will manage delivery milestones, sign off on data architectures, designs, data catalogs, and test strategies, coordinating with business and technical teams. The role requires deep insurance domain knowledge, strong backlog management, and collaboration across Data Architects, Data Engineers, BI developers, and IT leadership.

Qualifications

• Bachelor’s degree required in IT, CS, engineering, data analytics, or a closely related field.
• At least 5 years of experience in business/data analysis, software engineering, or data engineering roles within insurance or related domains.
• Experience with data architecture, data modeling, data governance, data quality, data security, and data visualization.

Responsibilities

• Manage project execution and deliver data products within the Finance domain for Data Marketplace.
• Own product requirements, user stories, acceptance criteria, and backlog items.

Skills

Data product ownership

Agile / Scrum

Stakeholder management

SQL

Power BI

Python

Data modeling

Data governance

Education

Bachelor's degree in Information Technology, Computer Science, Computer/Electronics Engineering, Business Data Analytics

Tools

Azure DevOps

R

Dynamics 365

Guidewire

Duck Creek

Job description

Munich Reinsurance America, Inc. seeks a Data Product Owner to lead data product work within the Finance domain for Data Marketplace initiatives.

You will manage delivery milestones, sign off on data architectures, designs, data catalogs, and test strategies, coordinating with business and technical teams. The role requires deep insurance domain knowledge, strong backlog management, and collaboration across Data Architects, Data Engineers, BI developers, and IT leadership.