Senior Data Engineering Lead

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

Design, build, and optimize scalable batch, streaming, and near-real-time data pipelines across cloud and enterprise data platforms

Develop AI-ready data products with solid focus on data quality, lineage, metadata, governance, observability, and reusability

Build semantic and knowledge layers that define business meaning, entity relationships, ontology structures, and reusable context for AI agents and applications

Implement GenAI data patterns including embeddings, vector search, semantic retrieval, RAG pipelines, hybrid search, and knowledge graph-based retrieval

Work hands-on with Python, SQL, PySpark, Spark, Databricks, Snowflake, Kafka, and cloud-native data services

Develop backend services, REST APIs, microservices, and data access layers using Python frameworks such as FastAPI, Flask, or equivalent

Integrate structured and unstructured data sources into governed, searchable, and AI-consumable data products

Build data interfaces that support analytics, ML models, GenAI applications, AI agents, and enterprise workflows

Collaborate with AI/ML engineers, applied scientists, platform engineers, product teams, and business stakeholders to translate data needs into scalable technical solutions

Ensure pipelines and data services are secure, reliable, observable, cost-efficient, and production-ready

Apply CI/CD, testing, monitoring, logging, alerting, and operational best practices for data pipelines and API services

Support enterprise standards for data governance, access control, privacy, compliance, and responsible AI usage

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

• Graduate degree or equivalent experience
• 8+ years of hands-on experience in data engineering, data platforms, backend engineering, or related roles
• Proven experience building ETL/ELT pipelines, data models, lakehouse patterns, and cloud-based data platforms
• Experience with Databricks, Snowflake, BigQuery, Azure Synapse, or similar modern data platforms
• Hands-on experience developing REST APIs, microservices, and backend data services using Python
• Experience working with structured, semi-structured, and unstructured data for analytics, ML, and GenAI use cases
• Experience with cloud platforms such as Azure, AWS, or GCP
• Practical exposure to GenAI data enablement including RAG, embeddings, semantic search, vector databases, and LLM-powered data workflows
• Solid hands-on expertise in Python, SQL, PySpark, Spark, and distributed data processing
• Solid understanding of data quality, lineage, metadata management, data catalogs, access controls, and observability
• Proven solid problem-solving, debugging, communication, and cross-functional collaboration skills

Preferred Qualifications

• Experience building semantic layers, ontology models, knowledge graphs, or graph-based data products
• Experience with Neo4j, Cosmos DB Gremlin, Amazon Neptune, RDF/SPARQL, Cypher, or GraphQL-based access layers
• Experience with vector databases such as Pinecone, FAISS, ChromaDB, Weaviate, pgvector, or Azure AI Search
• Experience supporting AI agents, MCP-style tools, GraphRAG, hybrid retrieval, or enterprise knowledge systems
• Experience with Airflow, Azure Data Factory, dbt, Kafka, Event Hub, Kinesis, or similar orchestration and streaming tools
• Experience with Docker, Kubernetes, CI/CD pipelines, GitHub Actions, Jenkins, or Azure DevOps
• Experience working in healthcare, insurance, financial services, or other regulated enterprise environments
• Familiarity with LangChain, LlamaIndex, Semantic Kernel, LangGraph, or similar AI orchestration frameworks

Technical Skills

Programming: Python, SQL, PySpark

Data Engineering: ETL/ELT, Spark, Kafka, Databricks, Snowflake, Lakehouse, Data Modeling

GenAI Data Layer: RAG, Embeddings, Vector Search, Semantic Search, Hybrid Retrieval

Semantic & Knowledge Layer: Ontology, Knowledge Graphs, Entity Resolution, Metadata, Business Glossary

Backend & APIs: FastAPI, Flask, REST APIs, Microservices, GraphQL

Cloud Platforms: Azure, AWS, GCP

Data Governance: Lineage, Catalogs, RBAC, IAM, Encryption, Audit Controls

DevOps: Docker, Kubernetes, CI/CD, GitHub Actions, Jenkins

Observability: Logging, Monitoring, Data Quality Checks, Pipeline Reliability

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.