Principal Architect

UnitedHealth Group•Eden Prairie, MN

15h•Remote

About The Position

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by diversity and inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health equity on a global scale. Join us to start Caring. Connecting. Growing together. Information is the lifeblood of the healthcare industry—everything depends on it. At Optum Insight Technology, you’ll help us work on streamlining the flow of information between payers, healthcare providers and various other stakeholders to deliver the right insights to the right places at the right times, driving better outcomes for patients, reducing friction in the health system and lowering costs. Every day our work directly impacts the world for the better, in meaningful and profound ways. We live in a time of unprecedented technical capability and possibility. Health care is at a pivotal point in this journey where even small gains can lead to major transformation. You could be a part of that – you have tremendous skill and the potential to make a lasting impact. Optum Insight Technology is uniquely positioned to bring your skills to bear on these pressing and life-changing technical challenges. The health care industry has an immediate need for your drive, innovation, passion and technical insight. Help us help the millions of people we serve each day. The Clinical Decision Support (CDS) Engineering team, a unit within the Optum Insight Technology organization is responsible for building commercial products helping payers and providers with administrative and clinician focused CDS solutions. We are seeking a Principal Data and Analytics Architect, responsible for defining, evolving, and governing enterprise data, analytics, and AI/ML architecture. This role provides hands-on architectural leadership for Azure and Data bricks based platforms, translating business and clinical needs into implemented reference solutions, production-ready patterns, and working prototypes that enable advanced ML/AI and enterprise scale BI. The position has a dual focus on ML/AI enablement and high quality BI and analytics required to measure, explain, and operationalize model performance across complex clinical and demographic data dimensions. The role ensures that analytics produced for executives, clinicians, researchers, and operational teams are consistent, trusted, explainable, and aligned with ML outcomes. CDS is an area that truly touches payers, providers, as well as members and one where you can have a huge impact on improving the healthcare system for everyone. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office for a minimum of four days per week.

Requirements

Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent practical experience
10+ years building large-scale data, analytics, or software platforms, with demonstrated hands-on delivery across multiple teams
5+ years leading enterprise data/analytics architecture for cloud platforms, including translating designs into implemented patterns and production rollouts
3+ years of experience with Azure and Databricks, including lakehouse patterns, Delta Lake, security/governance configuration, and workload optimization
3+ years of experience in Spark (PySpark/Scala) and Python for building data pipelines and reference implementations; strong SQL for analytics and performance tuning
3+ years of experience with analytical data modeling and semantic layer/metrics definitions; enabling BI platforms such as Power BI, Tableau, or similar
Experience implementing governance, security, and privacy controls for sensitive or regulated data (access control, encryption, auditability, retention)
Experience establishing engineering practices for data platforms (CI/CD, testing, monitoring/alerting, incident response)
Excellent communication skills with the ability to document and defend technical decisions and drive alignment through working sessions and reviews

Nice To Haves

Experience supporting ML/AI systems in production, including data/feature pipelines, experiment tracking, model monitoring, and measurable feedback loops
Experience with infrastructure-as-code and platform automation (e.g., Terraform/Bicep/ARM, Azure DevOps/GitHub Actions) for repeatable deployments and secure configuration
Experience with FinOps for data platforms (cost allocation/chargeback, workload optimization, and unit economics)
Familiarity with responsible AI practices (explainability, bias testing, governance)
Demonstrated ability to influence architecture decisions across organizational boundaries and drive alignment without direct authority
All employees working remotely will be required to adhere to UnitedHealth Group’s Telecommuter Policy.

Responsibilities

Own the end-to-end data, analytics, and AI/ML architecture vision and multi-year roadmap, and turn it into actionable designs (target architectures, sequence diagrams, contracts, and backlog-ready epics)
Create and maintain reference architectures and reference implementations for lakehouse / warehouse / streaming / semantic layer capabilities; publish reusable templates, accelerators, and golden paths
Lead architecture and design reviews, author architecture decision records (ADRs), and provide hands-on technical guidance through pairing, code/design feedback, and unblock teams on complex problems
Partner directly with engineering, product, analytics, data science, and security via working sessions to translate requirements into implemented solutions; communicate designs clearly to technical and non-technical stakeholders
Design and build scalable ingestion and transformation patterns (batch and streaming) using Databricks/Spark, Delta Lake, and orchestration tools; contribute code and reusable libraries where needed
Define and implement data modeling standards (conceptual/logical/physical; dimensional and other fit-for-purpose approaches) and guide teams through schema design, evolution, and review
Implement semantic/metrics layer approaches (shared definitions, certified datasets, KPI logic) that power consistent BI in Power BI/Tableau and downstream data products
Drive performance, reliability, resilience, and cost optimization through hands-on tuning (Spark/SQL), workload design, and infrastructure-as-code patterns; influence capacity planning with measured data
Implement scalable governance mechanisms (cataloging/classification, lineage, ownership, lifecycle) by configuring and integrating platform capabilities and tooling (e.g., Unity Catalog and enterprise catalog solutions as applicable)
Build data quality and observability into pipelines (automated checks, expectations, SLAs/SLOs, monitoring/alerting) and partner with teams to remediate issues and prevent recurrence
Design and implement security and privacy controls for regulated healthcare data (fine-grained access control, key management, audit logging, retention), and validate designs through threat modeling and review.
Partner with Risk/Compliance and Security to support audits and regulatory expectations (e.g., HIPAA, SOC2, SOX, GDPR/CCPA as applicable).
Establish and implement engineering standards for pipelines and analytics products (CI/CD, automated testing, code quality gates, schema/version management, environment promotion).
Instrument platforms and pipelines for reliability and freshness; build dashboards/alerts for pipeline health and cost-to-serve; lead incident response and postmortems with concrete corrective actions.
Enable ML/AI outcomes through hands-on data foundation work (feature availability, reproducibility, lineage) and integration patterns that support model training, scoring, and measurement at scale.
Produce and maintain architecture deliverables (ADRs, diagrams, runbooks, playbooks) that enable consistent implementation and operations across teams.
Mentor architects and senior engineers; raise organizational capability through communities of practice, coaching, and technical standards.
Support hiring and leveling by participating in interview loops and defining role expectations for data/analytics engineering talent.