Note: The job is a remote job and is open to candidates in USA. Optum is seeking a Principal Data Engineer for its Enterprise Finance Data Engineering team. The role designs and manages data architectures, pipelines, semantic layers, and AI-ready data products, partnering with finance, AI, and governance teams to deliver secure, trusted solutions.
Responsibilities
- 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
Skills
- 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 generative AI, machine learning, or large language model (LLM) data solutions in an enterprise production environment
- 2+ years of experience supporting AI/ML data patterns (e.g., embeddings, vector databases, document chunking, semantic retrieval, RAG architectures, or feature preparation)
- Experience with modern data governance, observability, and cataloging tools (e.g., Collibra, Monte Carlo, Alation, or Great Expectations)
- Proven stakeholder management and communication skills with the ability to bridge finance business requirements and technical data engineering solutions
- Proven ability to deliver iteratively in a fast-paced environment while upholding governance, data quality, and repeatability
Benefits
- Telecommute from anywhere within the U.S.
- A comprehensive benefits package
- Incentive and recognition programs
- Equity stock purchase
- 401k contribution
Company Overview
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