Lead Data Engineer - AI Platforms

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 inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.

Primary Responsibilities:

AI-First Data Platform Architecture

• Lead the architecture, design, and implementation of AI-first enterprise data platforms supporting AI/ML, Generative AI, Agentic AI, advanced analytics, and operational workloads
• Define enterprise data strategy, architecture standards, and AI-ready data platform capabilities
• Design end-to-end data architectures spanning:
  • Data ingestion
  • Data processing
  • Data storage
  • Data serving
  • Data governance
  • Data consumption
  • Semantic knowledge layers
• Architect modern data platforms using:
  • Lakehouse Architecture
  • Data Lakes
  • Data Warehouses
  • Medallion Architecture (Bronze, Silver, Gold)
  • Data Products
  • Data Mesh principles
• Design scalable architectures that support AI training, inference, Retrieval-Augmented Generation (RAG), Agentic AI, enterprise copilots, and advanced analytics

AI Data Foundations & Engineering

• Build AI-ready data platforms capable of supporting:
  • Feature stores
  • Embedding generation pipelines
  • Vectorized datasets
  • Semantic indexing
  • AI inference workloads
  • Enterprise knowledge retrieval systems
• Develop scalable ingestion frameworks supporting structured, semi-structured, and unstructured data at enterprise scale
• Architect distributed data systems utilizing partitioning, sharding, clustering, workload distribution, and parallel processing strategies
• Implement Lambda and Kappa architectural patterns to support batch, streaming, and real-time processing requirements
• Design highly resilient and fault-tolerant data pipelines utilizing:
  • Idempotency
  • Retry mechanisms
  • Checkpointing
  • Recovery strategies
  • Delivery guarantees
• Optimize storage formats, indexing, partitioning, query performance, and data lifecycle management for scalability and cost efficiency
• Design data architectures that appropriately balance consistency, availability, scalability, and latency requirements across distributed environments

Semantic & Knowledge Architecture

• Design and implement enterprise Semantic Layers that transform raw data into business-aware, AI-consumable knowledge assets
• Build semantic abstractions on top of structured, semi-structured, and unstructured data to expose business context, entity relationships, taxonomies, ontologies, and domain knowledge
• Architect intelligent knowledge platforms that unify data products, metadata, embeddings, lineage, knowledge graphs, and business semantics into a single AI-ready foundation
• Develop semantic models that enable AI systems, copilots, RAG applications, and Agentic AI workflows to understand context, meaning, relationships, and intent
• Create reusable semantic APIs and knowledge services that provide governed, context-aware access to enterprise knowledge assets
• Build architectures supporting:
  • Knowledge Graphs
  • Ontologies
  • Semantic Search
  • Vector Search
  • Enterprise Knowledge Repositories
  • Agent Memory Systems
  • Retrieval Platforms
  • AI Copilots and Agents
• Establish semantic interoperability standards that enable consistent AI consumption and enterprise-wide knowledge discovery

Governance, Security & Data Trust

• Establish enterprise-wide data governance frameworks including:
  • Data ownership and stewardship
  • Metadata management
  • Data cataloging
  • Data lineage
  • Data traceability
  • Data lifecycle governance
• Implement enterprise-grade data quality controls through validation, anomaly detection, deduplication, SLA monitoring, and automated quality enforcement
• Ensure compliance with regulatory requirements including HIPAA, GDPR, PII protection, and enterprise governance standards
• Define and enforce security controls including:
  • Role-Based Access Control (RBAC)
  • Encryption
  • Data masking
  • Access auditing
  • Secure data sharing
• Build secure and governed access layers supporting analytics, AI platforms, and downstream enterprise applications

Platform Reliability & Operational Excellence

• Implement end-to-end observability through monitoring, logging, lineage tracking, alerting, operational dashboards, and data platform telemetry
• Optimize enterprise data platforms for scalability, availability, reliability, performance, and cost efficiency
• Establish architecture standards, reusable patterns, and platform accelerators supporting enterprise-wide adoption
• Lead platform modernization, cloud transformation, and AI data platform initiatives
• Collaborate with AI/ML Engineers, Data Scientists, Applied Scientists, Platform Engineers, and Enterprise Architects to ensure data platforms effectively support model training, inference, RAG systems, and Agentic AI applications
• Mentor engineering teams and drive technical excellence across the organization

Builder Responsibilities:

• Design, develop, and deploy AI-powered solutions using no-code, low-code, and advanced platforms, translating business needs into scalable applications that enhance products, workflows, and decision-making
• 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 environment, 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 degree in Computer Science, Engineering, Information Systems, Data Engineering, or a related field (Master's degree preferred)
• 18+ years of experience in Data Architecture, Data Engineering, Enterprise Data Platforms, or related disciplines
• Experience designing platforms supporting TB-scale to PB-scale data workloads with high throughput and low latency requirements
• Proven experience designing and implementing large-scale enterprise data platforms supporting analytics, AI/ML, and AI-driven applications
• Experience building feature stores, semantic indexing solutions, embedding pipelines, vectorized datasets, and AI-ready data foundations
• Experience optimizing enterprise data platforms for performance, scalability, reliability, and cost efficiency
• Experience implementing secure data architectures using RBAC, encryption, data masking, auditing, and access controls
• Extensive experience building AI-first data platforms designed for AI/ML, Generative AI, Agentic AI, RAG, enterprise search, and intelligent applications
• Solid experience supporting knowledge retrieval systems, RAG architectures, semantic search, and enterprise knowledge platforms
• Deep knowledge of:
  • Data governance
  • Metadata management
  • Data lineage
  • Data quality frameworks
  • Data observability
• Solid understanding of regulatory requirements including HIPAA, GDPR, PII governance, and enterprise compliance standards
• Solid understanding of distributed systems, scalability engineering, workload distribution, storage optimization, and fault-tolerant architectures
• Deep expertise in modern architecture patterns including:
  • Lakehouse Architecture
  • Data Lake Architecture
  • Data Warehouse Architecture
  • Medallion Architecture
  • Lambda Architecture
  • Kappa Architecture
• Deep expertise in semantic data architecture, knowledge architectures, metadata-driven platforms, contextual data design, and AI-consumable data products
• Expertise in data modeling approaches including dimensional modeling, Data Vault, canonical models, feature modeling, and AI-oriented data structures
• Solid programming expertise in Python and SQL
• Proven ability to lead architecture strategy, influence executive stakeholders, and mentor engineering organizations

Preferred Qualifications:

• Hands-on experience with Databricks, Snowflake, Apache Spark, PySpark, Kafka, BigQuery, Synapse Analytics, Redshift, and modern cloud-native data platforms
• Experience architecting AI-first enterprise data platforms that support AI/ML, Generative AI, Agentic AI, enterprise search, knowledge retrieval, and intelligent application development
• Experience building enterprise knowledge and retrieval platforms utilizing metadata, embeddings, vector databases, semantic search, graph technologies, and context-aware retrieval services
• Experience developing AI-ready data products, feature stores, semantic APIs, reusable knowledge services, and data foundations that accelerate RAG, AI copilots, and Agentic AI initiatives
• Experience implementing Data Mesh, Data Products, Lakehouse, Medallion, Lambda, and Kappa architectures at enterprise scale
• Experience establishing enterprise standards for AI-ready data, semantic interoperability, metadata governance, trusted data consumption, and knowledge management
• Experience leading cloud modernization, AI platform transformation, and enterprise data strategy initiatives
• Experience building reusable platform accelerators, shared services, reference architectures, and enterprise engineering frameworks
• Experience within healthcare, financial services, banking, insurance, or other highly regulated environments
• Experience mentoring architects, engineering leaders, and enterprise technology teams
• Familiarity with vector databases, graph databases, semantic retrieval frameworks, AI serving architectures, and enterprise search platforms
• Deep expertise with modern AI-ready data platforms including Databricks Lakehouse Platform, Snowflake Data Cloud, Delta Lake, Apache Iceberg, and cloud-native data ecosystems
• Solid expertise in DataOps, CI/CD for data pipelines, automated testing, data observability, and platform engineering practices
• Expertise in designing enterprise Semantic Layers, Knowledge Graphs, Ontologies, Business Taxonomies, and Context Engineering frameworks that enable AI systems to consume data as business knowledge rather than raw information
• Contributions to enterprise architecture programs, technical publications, patents, open-source projects, or innovation initiatives

Technical Stack

• Data Platforms: Databricks (Lakehouse), BigQuery, Snowflake, Azure Synapse, Delta Lake
• Processing Engines: Apache Spark, PySpark, Spark SQL
• Streaming & Eventing: Kafka, Spark Streaming, Azure Event Hub, AWS Kinesis
• Storage Systems: S3, ADLS, GCS, HDFS, Parquet, ORC
• Databases: PostgreSQL, MySQL, SQL Server, Oracle, Cosmos DB, NoSQL systems
• AI Data Layer: Pinecone, ChromaDB, FAISS, Vector embeddings, Semantic search, RAG pipelines
• ETL & Orchestration: Apache Airflow, Azure Data Factory, dbt
• Programming: Python, SQL
• Cloud Platforms: AWS, Azure, GCP
• DevOps: Docker, Kubernetes, CI/CD (Jenkins, GitHub Actions)
• Observability & Governance: Monitoring, logging, tracing, data catalogs, lineage tools
• Security & Compliance: RBAC, IAM, encryption, masking, auditing, governance frameworks
• Integration: REST APIs, microservices, data-serving endpoints

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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