Role Summary An individual in the Architecture job family is a strategic resource for UnitedHealth Group.

This Senior Architect role within the Chief Data analytics Office (CDaO) is a hands-on technical leader who shapes the enterprise data architecture strategy, drives modernization of data platforms, and enables trusted, scalable, and AI-ready data foundations across the enterprise. This individual will be involved across many aspects of data architecture, design,

engineering, and implementation of solutions on modern lakehouse and cloud data platforms — providing technical leadership across product, engineering, and enterprise stakeholders. Responsible for communicating overall architecture vision, technical strategies, and trade-offs across various levels in the organization to gain buy-in. In this senior role, the individual leads enterprise data solutions and capabilities, and is a solid influencer that engages with client executives, business leaders, and enterprise architects.

Primary Responsibilities

Enterprise Data Architecture & Strategy

 Craft and communicate the overarching data architecture vision, solution intent, and technical strategies for the Chief Data Office, clearly articulating trade-offs and securing buy-in across product, engineering, and enterprise stakeholders

 Develop comprehensive architectural plans, reference architectures, and target-state blueprints that guide the implementation of integrated data platforms aligned with business goals, analytics roadmaps, and consumer journeys

 Collaborate with business, product, and enterprise architects to understand strategy and translate it into actionable technical guidance for data engineering, analytics, and

AI/ML teams

Data Platform Engineering (Databricks, Snowflake, Microsoft Fabric)

 Architect and evaluate modern data platforms including Databricks (Lakehouse, Unity

Catalog, Delta Lake, MLflow), Snowflake (Warehouse, Snowpark, Cortex, Horizon

Catalog), and Microsoft Fabric (OneLake, Synapse, Power BI, Direct Lake), and define interoperability patterns across them

 Define reference architectures for medallion (bronze/silver/gold) design, data mesh, data fabric, lakehouse, and hub-and-spoke patterns aligned with enterprise standards

 Guide selection and adoption of the right platform for the right workload based on performance, cost, governance, and analyst experience considerations

 Establish patterns for ingestion, transformation, semantic modeling, data sharing,

and cross-platform interoperability (e.g., Iceberg/Delta, open table formats, zero-

copy sharing)

 Partner with platform engineering teams to design secure, scalable, and cost-optimized data pipelines and analytics workloads

Architecture Artifacts, PoVs & Design Documents

 Create, maintain, and evolve high-quality architecture artifacts including target-

state architecture diagrams, current-state assessments, solution intents, HLD/LLD design documents, sequence diagrams, and data flow diagrams using tools such as

Lucidchart, Visio, draw.io, or Mermaid

 Author Points of View (PoVs), whitepapers, and technical briefs on emerging data and AI technologies, evaluating vendor capabilities, competitive positioning, and enterprise fit

 Present solution options, decision matrices, capability-by-capability comparisons, and clear recommendations with rationale to drive alignment and informed decision-

making at executive forums such as the Data Architecture Review Board (DARB)





 Maintain a library of reusable reference architectures, design patterns, and standards to ensure consistency, clarity, and reuse across delivery teams

Governance, Security & Compliance

 Ensure that solution architectures for both internally built and acquired capabilities conform to enterprise architecture standards, security policies, cloud best practices, and healthcare compliance requirements (HIPAA, HITRUST, PHI/PII handling)

 Embed data governance, lineage, cataloging, access controls, and quality validation early in the design process using tools such as Unity Catalog, Purview,

Collibra, or Snowflake Horizon

 Provide recommendations and technical guidance to improve scalability, performance,

reliability, security, and reusability within budget, resource, and business constraints

Stakeholder Engagement & Technical Leadership

 Engage with multiple delivery teams and vendors, offering hands-on architectural support through design reviews, architecture walkthroughs, and evaluation of emerging technologies

 Communicate architectural decisions, risks, and trade-offs effectively to a broad range of stakeholders, including senior leadership, in a clear and executive-ready manner

 Partner with delivery teams to identify, prioritize, and mitigate technical debt, balancing short-term delivery needs with long-term platform health

 Guide engineering teams to deliver secure, resilient solutions by embedding non-

functional requirements and enterprise guardrails early in the design process

AI-First Mindset & Innovation

 Apply an AI-first mindset, enabling teams to adopt AI-based and agentic approaches

(e.g., Databricks Genie, Snowflake Cortex, Fabric Copilot) where they provide measurable value

 Continuously enhance technical knowledge and apply innovative solutions using emerging technologies, including AI-enabled and cloud-native approaches

 Promote the use of shared services, reusable frameworks, and federated architecture patterns across engineering teams

Collaboration & Mentorship

 Operate as a self-motivated, self-directed architecture partner who proactively identifies gaps, risks, and opportunities across data initiatives

 Demonstrate solid collaboration skills by building trust-based relationships across product, engineering, TPM, security, and enterprise architecture teams

 Reviews the work of others; develops innovative approaches; sought out as expert;

serves as a leader/mentor

Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-

assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work setting, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so.

Required Qualifications





 Bachelor's or Master's degree in Computer Science, Data Science, or a related field

 10+ years of overall technology experience, including 5+ years in data architecture or senior technical leadership roles

 5+ years of experience designing, implementing, and integrating enterprise data solutions spanning data engineering, cloud platforms, system integration, solution design, development, configuration, and deployment

 5+ years of hands-on experience with modern data platforms — Databricks,

Snowflake, and/or Microsoft Fabric — including lakehouse design, warehouse modeling, semantic layers, and cross-platform interoperability

 5+ years of hands-on experience architecting and implementing solutions on public cloud platforms such as Azure, AWS, or Google Cloud

 5+ years of experience solving complex business problems involving multiple domains,

systems, and technologies in enterprise data environments

 5+ years of experience effectively communicating architectural solutions and patterns to geographically distributed teams, including senior leaders

 5+ years of demonstrated experience creating architecture diagrams, Points of

View (PoVs), reference architectures, and HLD/LLD design documents for executive and technical audiences

 Proficiency in SQL, Python, PySpark, and modern data engineering frameworks (Delta

Lake, Iceberg, dbt, Airflow)

 Solid understanding of data governance, cataloging, lineage, and metadata management tools (Unity Catalog, Purview, Collibra, Snowflake Horizon)

 Experience with semantic layer technologies (e.g., dbt Semantic Layer, AtScale, Cube,

Looker) and knowledge-graph-powered analytics is a strong plus

 Experience in designing and implementing AI solutions leveraging frontier models,

RAG, agentic architectures, vector DBs and semantic retrieval.

 Demonstrated experience facilitating and contributing to large-scale, architecturally significant data platform initiatives from conception through implementation

 Solid grounding in industry-standard architecture principles, design patterns, non-

functional requirements, and engineering best practices

 Proven excellent written and verbal communication skills, with the ability to influence and align stakeholders without direct authority

Preferred Qualifications

 Certifications in Databricks (Data Engineer/Architect), Snowflake (SnowPro

Advanced Architect), Microsoft Fabric (DP-600/700), or Azure Solutions Architect

Expert

 Experience with AI/ML platforms, generative AI, agentic AI, and MLOps practices

 Exposure to TOGAF, DAMA-DMBOK, or similar enterprise architecture / data management frameworks

 Experience contributing to Architecture Review Boards (ARB/DARB) and enterprise standards forums

At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone—of every race, gender,

sexuality, age, location and income—deserves the opportunity to live their healthiest life.

Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes — an enterprise priority reflected in our mission.

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