Join Optum, a global leader in healthcare technology dedicated to improving lives through innovative solutions. We are seeking a Data Engineering Lead to drive the development and optimization of our enterprise-scale data platforms. This role offers the chance to directly impact health outcomes by building robust data pipelines and empowering advanced analytics. If you are passionate about leveraging data to advance health optimization and thrive in a culture of inclusion and collaboration, this is an exceptional opportunity.

Our commitment extends to fostering a diverse and equitable environment where everyone can achieve their healthiest life. We are actively working to remove barriers to good health and deliver equitable care. Advance your career with us while making a significant difference.

Spearhead the design, development, and maintenance of scalable ETL/ELT data pipelines, ingesting data from diverse sources including databases, APIs, cloud platforms, and streaming feeds. Architect and implement enterprise data warehouses and data lakes, optimizing for performance and storage through advanced data modeling techniques like Star Schema and Snowflake Schema. Ensure data integrity and compliance by implementing rigorous data validation, quality checks, and adhering to governance, security, and RBAC standards.

Develop cloud-native data solutions on Azure, AWS, or GCP, managing services such as Azure Data Factory, Databricks, and Snowflake. Drive performance optimization for SQL queries, Spark jobs, and data pipelines, actively monitoring workloads and resolving failures to meet SLA commitments. Collaborate closely with Product Owners, Business Analysts, and Data Scientists to translate business requirements into scalable data solutions, supporting testing and production deployments.

Implement robust DevOps and CI/CD practices for data pipelines using tools like GitHub and Azure DevOps. Enable self-service analytics and empower reporting tools like Power BI and Tableau by providing clean, trusted datasets. Support the integration of AI/ML models and operational reporting, ensuring seamless data delivery and adherence to all company policies and flexible work arrangements.

We are looking for an experienced professional with a graduate degree or equivalent practical experience. Proven expertise in real-time and streaming data pipelines is essential, alongside hands-on experience with the Databricks and Azure ecosystem. Demonstrated success in implementing AI/agentic or LLM-based solutions and understanding of the AI-DLC lifecycle is highly valued.

The ideal candidate will have a proven track record of working on enterprise-scale data platforms, moving beyond proof-of-concept stages. Strong skills in data warehousing, data modeling (Star Schema, Snowflake Schema), and performance optimization are required. Familiarity with cloud platforms such as Azure, AWS, or GCP, including services like Azure Data Factory, Databricks, ADLS, Synapse, Snowflake, and Spark, is crucial.

Proficiency in SQL, CI/CD implementation, and version control tools like GitHub and Azure DevOps is expected. Experience in enabling reporting and analytics using tools like Power BI and Tableau, as well as supporting AI/ML initiatives, is also necessary. This role requires excellent collaboration and communication skills to effectively translate business needs into technical solutions.