Join Optum, a global leader in health optimization, and contribute to improving health outcomes through innovative technology. As a Senior Data Engineering Lead, you will play a crucial role in designing and implementing advanced data solutions that power AI and GenAI applications. Our inclusive culture fosters collaboration, professional growth, and comprehensive benefits, allowing you to make a significant impact on communities worldwide. We are committed to Caring, Connecting, and Growing together.

Design and optimize scalable batch, streaming, and near-real-time data pipelines across cloud and enterprise platforms. Develop AI-ready data products with a strong emphasis on data quality, lineage, governance, and reusability. Build semantic and knowledge layers crucial for AI agents and applications, implementing GenAI patterns like embeddings, vector search, and RAG pipelines. Work hands-on with technologies such as Python, SQL, PySpark, Spark, Databricks, Snowflake, and Kafka. Develop backend services, REST APIs, and microservices. Integrate diverse data sources into governed, AI-consumable products. Collaborate with cross-functional teams to translate data needs into robust technical solutions, ensuring data pipelines and services are secure, observable, and cost-efficient. Apply CI/CD, testing, and monitoring best practices, adhering to enterprise standards for data governance, privacy, and responsible AI.

A Graduate degree or equivalent experience is required. Possess over 8 years of hands-on experience in data engineering, data platforms, or backend engineering. Proven track record in building ETL/ELT pipelines, data models, lakehouse patterns, and cloud-based data platforms using tools like Databricks, Snowflake, or BigQuery. Expertise in developing REST APIs, microservices, and backend data services with Python is essential. Experience in handling structured, semi-structured, and unstructured data for analytics, ML, and GenAI use cases. Practical exposure to GenAI data enablement, including RAG, embeddings, and semantic search, is required. Solid hands-on skills in Python, SQL, PySpark, and Spark are fundamental. A strong understanding of data quality, lineage, metadata management, data catalogs, access controls, and observability is necessary. Excellent problem-solving, debugging, communication, and cross-functional collaboration skills are a must.