Principal Data Engineering - Hybrid

About the role

The Principal Data Engineering professional within the Optum Insight Engineering AI team will design and develop robust public cloud data systems, services, and reusable patterns that drive massive scale and performance. The team aims to deliver secure, private, and highly scalable Azure cloud solutions that enable the organization to utilize clean, reliable data safely and swiftly.

Responsibilities

• 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, LakeBase, 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
• 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

Qualifications

• 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 DevOps automation using Terraform
• 3+ years of experience with CI/CD processes and tools (such as GitHub Actions, GIT, Artifactory, or Sonar)
• 2+ years of experience building LLM integrations for workflow automation or business needs

Preferred Qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related discipline
• Healthcare and Provider domain experience
• Experience working with LLMs
• Extensive knowledge of data architecture principles (e.g., Data Lake, Databricks Delta Lake, Data Warehousing, etc.)
• Extensive knowledge of data modeling techniques including slowly changing dimensions, aggregation, partitioning, and indexing strategies
• Proven ability to independently troubleshoot and performance tune large-scale enterprise systems
• Proven excellent collaborator with experience working effectively with cross-functional teams such as leadership, product management, and engineering, with a willingness to inspire other data engineers, data scientists, and analysts
• Proven solid communication skills with the ability to communicate technical concepts to both technical and non-technical audiences

Benefits

Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements).