Data Engineering Consultant

Requisition Number: 2348563
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.
The Data Engineer (SG27) will play a critical role in designing, building, and maintaining enterprise-scale data platforms that support advanced analytics, AI/ML initiatives, and intelligent automation. This role focuses on delivering reliable, scalable, and high performance data pipelines using Azure Databricks, Azure Cloud services, and Snowflake, while enabling data science, AI/ML, and Large Language Model (LLM) use cases.
The individual will work closely with data scientists, AI/ML engineers, and business stakeholders to translate complex data requirements into robust data solutions. The role also emphasizes integration of ML models, LLM-powered solutions, and conversational AI experiences using Microsoft Copilot Studio, ensuring solid governance, security, and operational excellence across the data lifecycle.
Primary Responsibilities:

• Design, develop, and maintain scalable, fault-tolerant data pipelines using Azure Databricks and Apache Spark to process large volumes of structured and unstructured data
• Implement and optimize ETL / ELT workflows on Azure Cloud, ensuring high performance, reliability, and cost efficiency
• Integrate, manage, and optimize data storage and retrieval in Snowflake, supporting analytics, reporting, and AI/ML workloads
• Collaborate closely with data scientists, analysts, and business stakeholders to understand data requirements and deliver end-to-end data solutions aligned with business goals
• Ensure data quality, consistency, and integrity across ingestion, transformation, and consumption layers through validation, monitoring, and governance practices
• Proactively troubleshoot and optimize data pipelines for performance bottlenecks, scalability challenges, and operational issues
• Develop and maintain technical documentation for data architectures, pipelines, workflows, and operational procedures
• Partner with AI/ML teams to integrate machine learning models into production data pipelines for predictive analytics and automation use cases
• Support deployment, versioning, and monitoring of ML models using tools such as MLflow, Azure ML, and Databricks ML
• Work with Large Language Models (LLMs) to build intelligent data solutions, including prompt engineering, API-based integrations, and contextual data grounding
• Leverage Microsoft Copilot Studio to design and implement conversational AI experiences that enhance data accessibility and user interaction
• Embed LLM and AI capabilities into data and development pipelines using secure API integration, context management, and governance best practices
• Stay current with emerging trends, tools, and best practices in data engineering, cloud platforms, AI/ML, and generative AI technologies
• Provide technical guidance and mentorship to junior engineers, promoting engineering excellence and best practices across the team
• 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, Information Technology, Engineering, or a related field
• 7+ years of hands-on experience in data engineering, with solid exposure to cloud-based data platforms and AI/ML integration
• Hands-on experience with Pandas, NumPy, PySpark, and distributed data processing frameworks
• Experience with CI/CD pipelines, version control systems (Git), and containerization technologies such as Docker and Kubernetes
• Proven experience with Azure Databricks, Azure Cloud services (ADF, ADLS, Azure ML, etc.), and Snowflake
• Solid SQL skills and experience working with relational, cloud-native, and analytical databases
• Solid understanding of Apache Spark and large-scale distributed data architectures
• Solid proficiency in Python for data processing and automation
• Proven solid communication and collaboration skills to work effectively across technical and business teams
• Proven excellent analytical, troubleshooting, and problem-solving abilities with high attention to detail
• Proven ability to work independently, manage multiple priorities, and deliver high-quality solutions in a fast-paced environment

Preferred Qualification:

• Familiarity with Microsoft Copilot Studio or conversational AI platforms

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.