Principal Data Engineering - Hybrid| Eden Prairie, MN

Principal Data Engineering

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities.

Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.

As a Principal Data Engineering professional within the Optum Insight Engineering AI team, you will design and develop robust public cloud data systems, services, and reusable patterns that drive massive scale and performance. Our team's vision is to deliver secure, private, and highly scalable Azure cloud solutions that enable the organization to utilize clean, reliable data safely and swiftly. In this role, you will lead the creation of modern ETL/ELT pipelines, Spark workflows, and AI integrations.

Working with advanced platforms such as Azure Databricks, Snowflake, and LLMs, you will build data solutions that empower machine learning, insights generation, and business automation, directly contributing to better clinical and administrative connectivity across the healthcare system.

If you are located in Eden Prairie, you will have the flexibility to work remotely*, as well as work in the office as you take on some tough challenges. This position follows a hybrid schedule with four in-office days per week.

• Design and Develop Scalable Cloud Applications:
  Develop services, controls, and reusable patterns (such as microservices and Azure functions) that enable the team to deliver value safely, quickly, and sustainably in the Azure public cloud while enabling security and privacy at scale
• Build and Optimize Large-Scale Data Pipelines:
  Design, build, optimize, and manage modern large-scale data pipelines and ETL/ELT processing on Azure Databricks, Lake Base, and Apache Spark to support data integration, analytics, machine learning features, and predictive modeling
• Deploy AI and Data-Driven Solutions:
  Develop and deploy large-scale data pipelines empowering machine learning algorithms, insights generation, business intelligence dashboards, reporting, and new data products while utilizing enterprise-approved AI tools to address complex business challenges
• Develop AI-Powered Business Solutions:
  Build AI-based solutions for solving business needs, automating processes, and streamlining workflows to drive operational efficiency and continuous improvement
• Architectural Evolution and Standards:
  Participate in the architectural evolution of data engineering patterns, frameworks, systems, and platforms, including defining best practices and standards for managing data collections and integrations
• Improve System Quality and Data Reliability:
  Write advanced, complex SQL with performance tuning and optimization to identify and implement ways to improve data reliability, data integrity, system efficiency, and overall quality
• Collaborative Leadership and Mentoring:
  Foster high-performance, collaborative technical work resulting in high-quality output. Mentor other data engineers, providing technical direction and training on leveraging cloud data platforms
• Stakeholder and Requirement Analysis:
  Intersect skillfully with business stakeholders and third-party technical organizations to understand new product capabilities, decompose implementations into specific functional changes, analyze data for decision-making, and provide detailed, realistic estimates
• Evaluate Emerging Trends:
  Evaluate emerging trends to inform solution design, strategic innovation, and the evolution of cloud data architectures
• Best Practices in performance scalability and optimization

You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.

• Bachelor's degree or equivalent experience (such as an additional 8+ years of data engineering experience)
• 10+ years of experience in data engineering, data integration, data modeling, data architecture, and ETL/ELT processes
• 7+ years of experience in Python
• 5+ years of experience in Apache Spark (PySpark/Spark SQL)
• 5+ years of experience in SQL, including designing complex data schemas and query performance optimization
• 3+ years of experience with API design and lifecycle management (GraphQL, REST, etc.)
• 3+ years of experience building and deploying cloud-based solutions using Azure Databricks with UC, Snowflake, Functions, or Service Bus
• 3+ years of experience with Dev Ops automation using Terraform
• 3+ years of experience with CI/CD processes and tools (such as Git Hub Actions, GIT, Artifactory, or Sonar)
• 2+…