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.
Primary Responsibilities:
•Data Modeling & Analytics Engineering - Design, develop, and maintain scalable dimensional data models, including Star and Snowflake schemas
•Build curated data products that support reporting, analytics, AI, and machine learning use cases
•Define and standardize business logic, KPIs, metrics, and calculations across enterprise domains
•Create reusable datasets, semantic models, and data assets to improve consistency and reduce duplication
•Collaborate with business stakeholders to translate requirements into analytical and reporting solutions
•Databricks Platform Engineering - Design and implement modern data architectures leveraging Databricks Lakehouse principles
•Develop and maintain Bronze, Silver, and Gold data layers
•Build scalable ETL/ELT pipelines using Spark, PySpark, SQL, and Databricks Workflows
•Optimize Delta Lake implementations using partitioning, Z-Ordering, data skipping, Change Data Feed (CDF)
•Delta optimization techniques - Implement CI/CD pipelines and DevOps best practices for data engineering and analytics solutions
•Context Layer & Semantic Layer Development
•Design and manage enterprise semantic and context layers to provide a single source of truth for reporting and analytics
•Standardize business metrics, dimensions, hierarchies, and definitions across reporting platforms
•Enable self-service analytics through Power BI, Tableau, and AI-driven applications
•Manage semantic models and governance processes to ensure reporting consistency
•Establish metric certification, lineage tracking, and data governance standards
•Performance Optimization & Cost Management - Analyze query execution plans and identify performance bottlenecks
•Optimize Spark workloads for scalability, reliability, and cost efficiency
•Implement cluster sizing, autoscaling, caching, broadcast joins, and Adaptive Query Execution (AQE) strategies
•Tune Delta Lake tables through file compaction, partition optimization, and storage management
•Monitor warehouse and cluster utilization to improve performance and control cloud expenses
•Enhance dashboard and reporting performance through optimized data models and query design
•Data Governance & Quality Management - Develop and implement data quality frameworks, validation processes, and automated monitoring
•Ensure compliance with enterprise security, governance, and regulatory requirements
•Support metadata management, data cataloging, and lineage initiatives
•Establish monitoring and alerting for data pipelines, processing failures, and data anomalies
•Partner with governance teams to drive data stewardship and certification processes
•Technical Leadership & Collaboration - Collaborate with data scientists, analysts, engineers, product owners, and business leaders
•Mentor and guide junior analytics engineers and data engineers
•Conduct architecture reviews and recommend technology best practices
•Drive adoption of enterprise data standards, frameworks, and governance processes
•Lead technical design discussions and influence data platform strategy and roadmap
•Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements
Required Qualifications:
•Graduate degree or equivalent experience
•Bachelor's degree in Computer Science, Information Systems, Data Engineering, Analytics, or a related field
•7+ years of experience in Data Engineering, Analytics Engineering, Business Intelligence, or related disciplines
•3+ years of hands-on experience with Databricks and Delta Lake technologies
•Hands-on experience with Power BI, Tableau, or similar BI platforms
•Experience building enterprise-scale ETL/ELT pipelines
•Experience implementing semantic layers and enterprise reporting solutions
•Experience with performance tuning of Spark workloads and large-scale analytics environments
•Experience with cloud platforms such as Azure, AWS, or Google Cloud
•Knowledge of CI/CD, DevOps practices, and version control systems such as Git
•Solid understanding of Databricks Lakehouse architecture and Medallion (Bronze/Silver/Gold) design patterns
•Solid understanding of data governance, metadata management, and data quality frameworks
•Solid expertise in dimensional data modeling, including Star and Snowflake schema design
•Advanced proficiency in SQL and Spark/PySpark development
•Proven excellent problem-solving, communication, and stakeholder management skills
