Director of Architecture - Remote

UnitedHealth Group•San Francisco, CA

2d•Remote

About The Position

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.

Requirements

10+ years of experience 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

Nice To Haves

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

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