[Hiring] Director of Architecture @UnitedHealth Group

Role Description

Optum Tech is a global leader in health care innovation. Our teams develop cutting-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care’s most complex challenges. Your contributions here have the potential to change lives. Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together.

You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges.

Primary Responsibilities:

• AI/ML Data Architecture Strategy & Leadership:
  • Partner with business, data science, and technology leaders to define and evolve enterprise AI/ML and data strategies, including data platforms, feature stores, and model lifecycle capabilities.
  • Establish and enforce data architecture principles, standards, and governance frameworks that support trustworthy, compliant, and reusable data for AI/ML use cases.
  • Lead the design of scalable data ecosystems that enable real-time and batch data processing, model training, inference, and monitoring.
  • Drive alignment between data architecture, ML engineering, and platform engineering to ensure seamless integration of AI capabilities into digital products.
• Data Platform & ML Ecosystem Design:
  • Assess current-state data and analytics environments to identify gaps in data quality, accessibility, lineage, and ML readiness.
  • Design and own target-state architecture for modern data platforms (e.g., lakehouse, data mesh, or hybrid architecture) optimized for AI/ML workloads.
  • Define reference architectures for key capabilities such as feature engineering, model training pipelines, MLOps, data observability, and model governance.
  • Develop end-to-end data and ML platform roadmaps, including ingestion, transformation, storage, feature management, and model deployment layers.
• Execution, Governance & Responsible AI:
  • Lead data and AI architecture governance to ensure alignment with enterprise standards, privacy, security, and responsible AI principles.
  • Establish data quality, lineage, and observability practices to support reliable model performance and regulatory compliance.
  • Align investments to prioritize reusable data assets, shared ML capabilities, and platform scalability.
  • Communicate the value of AI/ML data platforms and outcomes to senior stakeholders, including executives and business leaders.
• Innovation, Change & Talent Development:
  • Champion modern data and AI practices, including MLOps, DataOps, and continuous model improvement.
  • Stay current with advancements in AI/ML, data platforms, and cloud-native technologies, applying them to enterprise use cases.
  • Mentor and guide architects, data engineers, and ML engineers in building high-quality, scalable AI solutions.
  • Foster a culture of experimentation, responsible innovation, and data-driven decision-making.

Qualifications

• 10+ years of experience in Data Architecture, or closely related roles, with a solid focus on building data platforms for advanced analytics and machine learning.
• 5+ years of experience in AI/ML data Architecture.
• 5+ years of experience delivering complex, cross-domain data and AI solutions within large, matrixed organizations.
• Proven experience designing and implementing modern data architectures (e.g., lakehouse, data mesh) in cloud environments (Azure, AWS, or GCP).
• Hands-on or leadership experience with ML lifecycle components, including feature engineering, model training pipelines, deployment, and monitoring.
• Solid understanding of data governance, data quality, lineage, and privacy considerations in AI/ML contexts.
• Experience influencing senior leaders and translating AI/ML capabilities into business value.
• Ability to communicate complex technical concepts to non-technical stakeholders.

Requirements

• Bachelor’s or master’s degree in computer science, Data Science, Engineering, or a related field.
• Experience with MLOps frameworks and tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI, Databricks).
• Familiarity with feature stores, model registries, and experiment tracking systems.
• Experience with real-time data processing technologies (e.g., Kafka, Spark Streaming, Flink).
• Knowledge of responsible AI practices, including model explainability, bias mitigation, and governance frameworks.
• Experience with healthcare or regulated industry data environments.
• Prior experience mentoring or leading data, ML, or architecture teams.

Benefits

• A comprehensive benefits package.
• Incentive and recognition programs.
• Equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements).