Principal Data Engineer

UnitedHealth Group•Eden Prairie, MN

5d•$112,700 - $193,200•Remote

About The Position

The Enterprise Finance Data Engineering team is seeking a Lead Data Engineer (Principal Data Engineering) to design, build, and manage foundational data architectures that power enterprise finance analytics, automation, and modern AI solutions. The role partners closely with finance stakeholders, AI engineers, and governance teams to deliver trusted, scalable, and secure data products that drive business value. It involves engineering data pipelines, semantic layers, retrieval indexes, and retrieval-augmented generation (RAG) data patterns using cloud-based data platforms and integration technologies. The position may be performed remotely from anywhere within the U.S., subject to UnitedHealth Group’s Telecommuter Policy.

Requirements

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 such as 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.
Adhere to UnitedHealth Group’s Telecommuter Policy if telecommuting.
Pass a drug test before beginning employment.

Nice To Haves

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 such as embeddings, vector databases, document chunking, semantic retrieval, RAG architectures, or feature preparation.
Experience with modern data governance, observability, and cataloging tools such as 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.

Responsibilities

Design, build, and deploy data pipelines, transformations, semantic layers, retrieval indexes, and reusable data products for enterprise finance challenges, emphasizing 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 for finance solutions; assess 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.