Principal AI /Machine Learning Data Engineer - Remote or hybrid from MN or DC

UnitedHealth Group•Eden Prairie, MN

5h•Hybrid

About The Position

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The Enterprise Information Security (EIS) team is responsible for cybersecurity across the organization, supporting the business and members by reducing risk, rapidly responding to threats, focusing on business resiliency, and securing new acquisitions. The Principal AI / Machine Learning Data Engineer role focuses on designing and building scalable data platforms that enable advanced analytics, machine learning, and AI-driven solutions. This role will support the development of intelligent systems that process large-scale event and operational data, enabling faster insights, automation, and decision-making across the organization. This position sits at the intersection of data engineering, machine learning, and AI, with an emphasis on building modern data pipelines and enabling production-grade AI capabilities.

Requirements

Bachelor’s degree or equivalent experience
5+ years of experience designing, building, and operating production data pipelines and platforms
5+ years of hands-on development with Python (preferred) and/or Java, including code reviews, packaging, and deployment
5+ years of experience with Spark (PySpark) and Databricks (or similar distributed data processing platform)
2+ years of experience leveraging and deploying Generative AI use cases to production environments
Solid SQL skills and experience working with data lakes and warehouses (e.g., Databricks, Snowflake)
Experience building ingestion frameworks for structured and unstructured data (e.g., event/log, semi-structured JSON), including parsing and enrichment patterns
Experience designing and scaling ELT/ETL frameworks with orchestration tools such as Airflow (or equivalent)
Experience implementing data quality, observability, and monitoring practices (e.g., data quality checks, pipeline SLAs/SLOs, alerting)
Experience with metadata, lineage, and governance concepts and tooling (e.g., data catalogs, lineage, access controls)
Experience with data modeling best practices for analytics and ML use cases
Experience with DevOps and CI/CD practices and tools (e.g., GitHub Actions), containerization, and infrastructure-as-code (e.g., Docker, Kubernetes, Terraform)
Experience supporting ML/AI workflows (feature engineering, data preparation, and model deployment enablement); exposure to MLOps practices is a plus
Demonstrated ability to partner with cross-functional stakeholders, translate requirements into technical solutions, and lead through influence

Nice To Haves

Experience with cloud platforms such as AWS, Azure, or Google Cloud, including managed data services
Experience with streaming and event-driven architectures (e.g., Kafka, Kinesis, Event Hubs)
Experience with data quality and validation frameworks (e.g., Great Expectations, Deequ) and/or data observability tooling
Experience enabling MLOps practices (e.g., feature stores, model registries, experiment tracking, deployment automation)
Experience with lakehouse architectures, Delta Lake, and advanced Spark optimization/performance tuning
Experience with data visualization tools and libraries such as Plotly, seaborn, and Chartjs
Experience with machine learning and predictive analytics
Familiarity with security and privacy concepts for data platforms (e.g., least privilege, PII/PHI handling) and working with compliance partners

Responsibilities

Design, develop, and maintain scalable data pipelines and data platforms supporting analytics, machine learning, and AI use cases
Build and optimize ingestion frameworks for large-scale structured and unstructured data, including streaming and event-driven sources
Partner with cross-functional stakeholders to understand evolving data and AI needs and define long-term technical solutions
Enable and support machine learning and AI workflows, including feature engineering, data preparation, and model deployment support
Drive strategic initiatives around Generative AI, data quality, observability, lineage, and governance
Develop and maintain frameworks that support rapid experimentation and deployment of AI/ML solutions
Introduce and evolve best practices in data modeling, orchestration, testing, and monitoring
Identify and champion opportunities for platform scalability, performance optimization, and cost efficiency
Collaborate with product, analytics, and infrastructure teams to deliver high-impact data and AI solutions
Build and maintain reusable parsing, enrichment, analytic, and service libraries to accelerate delivery across teams
Work comfortably under time-sensitive conditions while ensuring thoroughness
Maintain high ethical standards and the ability to remain objective and confidential