Senior Data Engineering Consultant

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:

• Data Engineering and Architecture

  • Design and build scalable, secure, and high-performance data platforms on Microsoft Azure
  • Develop batch and real-time data pipelines using Databricks, Spark, and Azure Data Factory
  • Implement Medallion Architecture (Bronze, Silver, Gold) and Lakehouse design patterns
  • Optimize data processing workflows for performance, cost, reliability, and scalability
  • Build enterprise data models supporting analytics, AI/ML, and reporting use cases

• Azure and Databricks

  • Develop and maintain solutions using Azure Data Lake, Azure Databricks, Azure Synapse, Event Hub, and Azure SQL
  • Leverage PySpark, Spark SQL, Delta Lake, and Databricks Workflows for large-scale data processing
  • Implement monitoring, performance tuning, and governance within Azure environments
  • Support cloud migration and modernization initiatives

• Generative AI and LLM Enablement

  • Build AI-ready data pipelines to support LLM, RAG, and Agentic AI solutions
  • Integrate vector databases, embeddings, semantic search, and knowledge retrieval frameworks
  • Collaborate with Data Scientists and AI Engineers to develop GenAI applications
  • Ensure data quality, lineage, governance, and compliance for AI workloads
  • Support LLM operationalization and production deployment

• CI/CD, DataOps and MLOps

  • Design and implement CI/CD pipelines using Azure DevOps and GitHub Actions
  • Automate deployment of data engineering artifacts, notebooks, workflows, and infrastructure
  • Implement Infrastructure as Code (Terraform/Bicep)
  • Establish DataOps best practices for version control, testing, release management, and monitoring
  • Enable automated testing, quality validation, and deployment governance

• Collaboration and Leadership

  • Partner with business stakeholders, architects, AI/ML teams, and product owners
  • Mentor junior engineers and promote engineering best practices
  • Contribute to architecture reviews, technology roadmaps, and innovation initiatives
  • Drive adoption of enterprise data governance, security, and Responsible AI standards

• AI 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 or Master's degree in Computer Science, Engineering, Information Systems, or related field
• 8+ years of experience in Data Engineering and Cloud Data Platforms
• 5+ years of hands-on experience with Azure Data Services
• 4+ years of experience with Azure Databricks and Apache Spark
• Experience designing large-scale ETL/ELT pipelines
• Hands-on experience with Azure DevOps, Git, CI/CD automation, and Infrastructure as Code
• Solid expertise in Python, PySpark, SQL, and data modeling
• Knowledge of data governance, security, and compliance frameworks

• Preferred Qualifications:

• Azure Data Engineer Associate or Azure Solutions Architect certification
• Experience with LLMs, RAG architectures, Vector Databases, LangChain, Semantic Kernel, or similar frameworks
• Experience with MLflow, MLOps, and model deployment frameworks
• Experience working in enterprise-scale AI/ML environments
• Exposure to Agentic AI and Generative AI application development
• Familiarity with Kubernetes, Docker, and containerized deployments

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.

• Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or related field
• 8+ years of experience in Data Engineering and Cloud Data Platforms
• 5+ years of hands-on experience with Azure Data Services
• 4+ years of experience with Azure Databricks and Apache Spark
• Experience designing large-scale ETL/ELT pipelines
• Hands-on experience with Azure DevOps, Git, CI/CD automation, and Infrastructure as Code
• Solid expertise in Python, PySpark, SQL, and data modeling
• Knowledge of data governance, security, and compliance frameworks
• Azure Data Engineer Associate or Azure Solutions Architect certification
• Experience with LLMs, RAG architectures, Vector Databases, LangChain, Semantic Kernel, or similar frameworks
• Experience with MLflow, MLOps, and model deployment frameworks
• Experience working in enterprise-scale AI/ML environments
• Exposure to Agentic AI and Generative AI application development
• Familiarity with Kubernetes, Docker, and containerized deployments