Principal Data Engineer (remote)

MedStar Health; United States; Remote; USD 100,000 - 160,000; Full time

Job summary

MedStar Health is seeking a passionate Principal Data Engineer to contribute to building a next-generation analytics platform. The role involves technical specification documentation, data solution support, and collaboration with teams to create innovative data solutions.

Responsibilities

• Collaborate with business users and technology teams to understand data needs.
• Optimize BI dashboard quality and establish best practices.
• Design semantic data layer for analytics and AI.

Skills

Collaboration

Data Visualization

Data Engineering

Business Intelligence

Analytics

Job description

At Claritev, we pride ourselves on being a dynamic team of innovative professionals. Our purpose is simple - we strive to bend the cost curve in healthcare for all. Our dedication to service excellence extends to all our stakeholders - internal and external - driving us to consistently exceed expectations.

We are intentionally bold, we foster innovation, we nurture accountability, we champion diversity, and empower each other to illuminate our collective potential.

Be part of our amazing transformational journey as we optimize the opportunity towards becoming a leading technology, data, and innovation voice in healthcare. Onward and Upward!!!

JOB SUMMARY: We are looking for a passionate Principal Data Engineer to join us in building the next generation data analytics platform, creating technical specification documents, and providing support for the data solutions across the enterprise.

JOB ROLES AND RESPONSIBILITIES:

• Collaborate with business users, technology teams, and executives to understand their data needs to create innovative solutions
• Optimize BI dashboard quality, consistency, usability, and reliability.
• Establish BI dashboards best practices.
• Design semantic data layer for analytics and AI.
• Work with data science, data engineering, and software engineering teams to automate data analysis and visualization, advise on data transformation, and explore ways to design and develop data infrastructure.