Join Optum, a global health leader, and leverage technology to enhance lives. This role offers the opportunity to significantly improve health outcomes by connecting individuals with essential care, pharmacy benefits, data, and resources. You'll be part of an inclusive culture, collaborating with talented professionals and benefiting from comprehensive support and career development. Contribute to advancing global health optimization and be part of our journey to Care, Connect, and Grow.

The Data Engineering Lead will architect, design, and implement scalable data platforms and pipelines. This role involves leading end-to-end data engineering efforts, ensuring system reliability, performance, and cost-efficiency. Mentoring junior and senior data engineers, fostering technical excellence, and collaborating with analytics, product, engineering, and business teams to align data solutions with strategic objectives are key aspects of this position.

Key responsibilities include architecting and implementing scalable data platforms, pipelines, and ETL/ELT workflows. You will lead comprehensive data engineering initiatives, focusing on reliability, performance, and cost-effectiveness. Guiding and mentoring data engineering teams to cultivate technical excellence and collaborative practices is essential. Collaboration with cross-functional teams ensures data solutions effectively support business goals.

Develop, optimize, and maintain robust data pipelines using Python, SQL, and Apache Airflow. Manage large-scale data ingestion, transformation, and orchestration frameworks. Optimize database queries, stored procedures, and schema designs within MSSQL and other SQL/NoSQL environments. Build and manage cloud-native data platforms on AWS, including services like S3, Glue, Lambda, RDS, Redshift, EC2, and Step Functions. Ensure high availability, disaster recovery, and monitoring for critical data workflows, implementing CI/CD and DevOps practices.

Design and enhance enterprise data warehouses and data marts according to best practices. Drive dimensional modeling, data partitioning, performance tuning, and metadata management to ensure consistency and quality across analytical datasets. Establish data quality frameworks, lineage tracking, validation checks, and documentation. Implement governance standards covering security, compliance, and access controls, partnering with stakeholders to define master and reference data strategies. Adherence to all company policies, procedures, and directives is required.

A Bachelor's degree or equivalent experience is required, along with over 8 years of experience in Data Engineering, including 6+ years in a leadership capacity. More than 3 years of hands-on experience with AWS Cloud for data architecture and pipeline development, particularly with AWS Glue, is essential.

Extensive experience with Airflow for workflow orchestration and solid expertise in MSSQL, including query optimization, indexing, stored procedures, and performance tuning, are necessary. Proficiency in Python for data engineering (e.g., Pandas, PySpark) and solid knowledge of data management principles—governance, quality, lineage, and security—are crucial. A strong understanding of Data Warehousing, ETL/ELT processes (Airflow, Informatica/SSIS), and big data processing is also required.

Additional requirements include solid knowledge in building Machine Learning models, Generative AI, and Multi-Agent Systems (Google ADK is preferable). Proven excellent leadership, communication, and cross-team collaboration skills are vital. The ability to translate business needs into scalable technical designs is a key requirement for this role.