Senior Data Engineer

UnitedHealth Group•Eden Prairie, MN

5d•$91,700 - $163,700•Remote

About The Position

The Enterprise Finance Data Engineering team is seeking a Senior Data Engineer to design, build, and manage data foundations that power enterprise finance analytics, automation, and AI solutions. The role partners with finance stakeholders, AI engineers, and governance teams to deliver trusted, scalable, and secure data products that create measurable business value. The engineer will architect and implement data pipelines, transformations, semantic layers, and retrieval-augmented generation (RAG) data flows across modern cloud platforms to enable financial intelligence and automated workflows. The position offers the flexibility to telecommute from anywhere within the U.S. The salary range is $91,700 to $163,700 annually based on full-time employment.

Requirements

5+ years of experience in data engineering, data platforms, or related technical roles.
5+ years of hands-on experience with SQL, API integration, ETL/ELT development, and data integration across batch and streaming architectures.
3+ years of experience designing, deploying, and maintaining data pipelines on cloud-based data platforms such as Azure, AWS, GCP, or Snowflake.
3+ years of experience with data modeling, data quality management, metadata, and data lineage practices.
2+ years of experience implementing enterprise data security, privacy, and access control governance.

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 AI, machine learning, or generative AI solutions in an enterprise production environment.
Familiarity with modern data governance, observability, and cataloging tools such as Collibra, Monte Carlo, Alation, or Great Expectations.
2+ years of experience supporting or implementing AI data patterns such as embeddings, vector databases, document chunking, semantic retrieval, RAG architectures, or feature engineering.
Experience preparing synthetic, masked, or de-identified datasets for test and validation environments.
Strong problem-solving, communication, and stakeholder management skills.
Proven ability to move quickly while maintaining trust, governance, 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 automated data refresh processes.
Identify and evaluate data sources, systems of record, reference data, documents, and operational data for finance solutions, assessing data quality, completeness, lineage, sensitivity, and accessibility.
Collaborate with security, privacy, compliance, and data governance teams to ensure appropriate data handling, sensitivity controls, and regulatory compliance.
Develop synthetic, masked, or de-identified datasets for development, testing, and experimentation environments.
Implement data quality monitoring, validation controls, lineage documentation, and 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 tasks, and drive continuous improvement.