Principal Data Engineer

UnitedHealth Group•Eden Prairie, MN

1d•$112,700 - $193,200•Remote

About The Position

As a Principal Data Engineer on our Optum Technology team, you will drive data architecture, platform engineering, and AI/ML data pipeline integration to build scalable, resilient, and performant enterprise data systems. In this role, you will lead the technical design, development, and deployment of complex big data infrastructure and AI-powered data solutions. You will collaborate with software engineers, data scientists, product managers, and business leaders to build robust data pipelines, deploy machine learning applications into production, and convert healthcare data into actionable insights while emphasizing responsible AI practices.

Requirements

HS Diploma required
8 years of data engineering experience designing, building, and deploying enterprise-scale data architecture and pipelines
5 years of experience with relational databases, NoSQL, and cloud data warehousing platforms (e.g., Snowflake, Databricks)
5 years of experience in data modeling (dimensional, relational, or data vault) and schema design
5 years of experience with programming languages such as Python, Scala, Java, or SQL
3 years of experience with big data processing frameworks (e.g., Spark, Hadoop) or real-time streaming technologies (e.g., Kafka, Flink)
3 years of experience with cloud platforms (AWS, Azure, or GCP), infrastructure as code, and automated CI/CD pipelines

Nice To Haves

Experience with MLOps frameworks, model monitoring tools, or feature store platforms (e.g., MLflow, Feast, Kubeflow)
Experience in the healthcare industry working with health data standards (e.g., FHIR, HL7, EDI) and healthcare security/compliance regulations
Deep expertise with cloud-native data services, enterprise data governance, and metadata management tools
Proven track record of architecting distributed systems handling large-scale unstructured and structured data sets
Experience implementing automated testing, data quality frameworks, and responsible AI validation within deployment pipelines
Demonstrated experience leading technical architecture design, governing data security standards, and mentoring software/data engineering teams
3 years of experience building, integrating, and deploying machine learning pipelines, AI models, or AI-powered data applications into production environments

Responsibilities

Design, develop, and deploy AI-powered data solutions, machine learning pipelines, and automations to address complex business challenges with an emphasis on responsible AI practices
Architect, design, and implement complex, scalable data models, transformation frameworks, and multi-platform solutions across full technology stacks and enterprise domains
Leverage broad expertise across big data, streaming, cloud, and relational database technologies to optimize system performance, eliminate unnecessary complexity, and build scalable scheduling and ETL/ELT pipelines
Evaluate emerging technology trends, AI/ML frameworks, and platform automation to inform solution design, platform strategy, and enterprise innovation
Champion adherence to enterprise security, compliance, and data governance standards, proactively identifying, mitigating, and addressing security gaps in solution design and operational workflows
Drive a culture of continuous quality by leading root-cause analyses and building diagnostic instrumentation to demonstrate the business value of automated CI/CD chains
Serve as a Subject Matter Expert (SME), mentor, and escalation point for complex technical challenges, fostering technical excellence across engineering teams
Translate complex technical concepts into clear business terms, partnering with executive leadership, cross-functional stakeholders, and customers to guide strategic decisions and digital channel solutions