Join Optum, a global leader dedicated to improving health outcomes through innovative technology. We are seeking a Principal Data Engineer to drive the evolution of our data platforms and solutions. This role offers a unique opportunity to connect people with the care and resources they need, fostering healthier communities worldwide.

Our inclusive culture values talented peers, comprehensive benefits, and robust career development. Contribute to advancing health optimization on a global scale, embodying our commitment to Caring, Connecting, and Growing together.

As a Principal Data Engineer, you will be instrumental in designing, developing, and maintaining scalable data pipelines and platforms that power analytics, machine learning, and AI initiatives. You will architect robust ingestion frameworks for diverse data types, including real-time streams.

Collaborate closely with cross-functional teams to anticipate and address evolving data and AI needs, translating them into long-term technical strategies. Champion strategic advancements in Generative AI, data quality, observability, and governance. Develop frameworks that accelerate AI/ML solution deployment and promote best practices in data modeling, orchestration, and monitoring.

Identify and implement opportunities for platform scalability, performance enhancement, and cost optimization. Build and maintain reusable libraries to expedite delivery across teams. Work effectively under pressure, upholding high ethical standards and confidentiality. You will also review the work of others, develop innovative approaches, and serve as a go-to expert and mentor.

We are looking for a seasoned professional with a Bachelor's degree or equivalent experience and over five years of hands-on expertise in building and operating production data pipelines and platforms. Proficiency in Python and/or Java, along with extensive experience using Spark (PySpark) and Databricks, is essential.

Demonstrated experience with cloud platforms such as AWS, Azure, or GCP, including secure data handling practices, is required. You should possess a strong understanding of batch and streaming workloads, distributed processing, and lakehouse/warehouse patterns. Experience with Generative AI use cases in production, particularly RAG-style architectures, is highly valued.

Familiarity with ELT/ETL frameworks, orchestration tools like Airflow, and implementing data quality, observability, and monitoring practices is crucial. Knowledge of metadata, lineage, and governance concepts, along with DevOps and CI/CD practices, containerization, and infrastructure-as-code, will set you apart.

Solid SQL skills and experience with data lakes and warehouses are a must. The ability to lead through influence, mentor engineers, and translate complex problems into scalable technical roadmaps is highly desired. Excellent collaboration skills to partner with stakeholders and translate requirements into technical solutions are key.