Principal Data Engineer

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, and data they need to feel their best. Here, you will find a culture guided by diversity and inclusion, talented peers, comprehensive benefits, and career development opportunities.

Come make an impact on the communities we serve as you help us advance health equity on a global scale. Join us to start
Caring. Connecting. Growing together.

The Enterprise Finance Data Engineering team is seeking a Lead Data Engineer (Principal Data Engineering) to design, build, and manage the foundational data architectures that power enterprise finance analytics, automation, and modern AI solutions. In this role, you will partner closely with finance stakeholders, AI engineers, and governance teams to deliver trusted, scalable, and secure data products that drive measurable business value.

You will play a pivotal role in engineering high-impact data pipelines, semantic layers, retrieval indexes, and retrieval-augmented generation (RAG) data patterns using cloud-based data platforms and advanced integration technologies.

You’ll enjoy the flexibility to telecommute
* from anywhere within the U.S. as you take on some tough challenges.

• Design, build, and deploy data pipelines, transformations, semantic layers, retrieval indexes, and reusable data products to address complex enterprise finance challenges with an emphasis on responsible AI use
• Partner with AI Engineers on RAG architectures, embeddings, vector databases, grounding strategies, and data refresh processes
• Identify and evaluate data sources, systems of record, reference data, documents, and operational data required to support finance solutions while assessing data quality, completeness, lineage, sensitivity, and accessibility
• Collaborate with security, privacy, compliance, and data governance teams to ensure appropriate data handling, access controls, and compliance
• Develop synthetic, masked, or de-identified datasets for development, testing, and experimentation environments
• Implement data quality monitoring, validation controls, lineage documentation, and automated exception‑handling processes
• Support experimentation, control‑group testing, and value measurement through reliable baseline and treatment datasets
• Evaluate emerging data and AI trends to inform solution architecture, design, and strategic innovation
• Use enterprise-approved AI tools to streamline workflows, automate data operations, and drive continuous improvement

You’ll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.

• 7+ years of experience in data engineering, data platform architecture, or related technical roles
• 5+ years of hands‑on experience with SQL, API integrations, ETL/ELT development, and batch or streaming data architectures
• 3+ years of experience building and optimizing large‑scale data pipelines on cloud‑based data platforms (e.g., Azure, AWS, GCP, or Snowflake)
• 3+ years of experience with data modeling, metadata management, data lineage, and enterprise data governance practices
• 2+ years of experience implementing enterprise data security, privacy, and access control standards

• Bachelor's degree or 4+ years of equivalent data engineering experience
• Experience supporting finance domains such as FP&A, accounting, tax, treasury, or financial reporting
• Experience enabling…