Note: The job is a remote job and is open to candidates in USA. Optum delivers technology-enabled healthcare services, helping people access care, pharmacy benefits, data, and other resources to improve health outcomes. The Principal Data Engineering professional will design and develop scalable Azure cloud data systems, ETL/ELT pipelines, Spark workflows, and AI integrations. The role also provides technical leadership, mentors data engineers, improves data reliability, and develops solutions supporting machine learning, business intelligence, and healthcare operations.
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
- 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
Skills
- 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
- 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
- Flexibility to work remotely from anywhere within the U.S. during Central time zone business hours, subject to UnitedHealth Group's Telecommuter Policy
- A comprehensive benefits package
- Incentive and recognition programs
- Equity stock purchase
- 401k contribution
Company Overview
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