Senior Data Engineer - Remote

UnitedHealth Group•Minnetonka, MN

8h•Remote

About The Position

For those who want to invent the future of health care, here's your opportunity. We're going beyond basic care to health programs integrated across the entire continuum of care. Join us to start Caring. Connecting. Growing together. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week. You’ll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.

Requirements

7+ years of experience in data engineering, software engineering, or similar disciplines
Hands-on experience with Databricks or Snowflake
Experience with orchestration tools such as Apache Airflow
Experience working with cloud ecosystems (Azure preferred; AWS/GCP acceptable)
Advanced SQL skills and experience with OLTP and OLAP data modeling
Solid understanding of modern data warehousing, data lake, and ELT/ETL design patterns
Familiarity with data governance tools, especially Microsoft Purview
Solid programming expertise in Python, PySpark, or similar languages
If you are offered this position, you will be required to provide extensive personal information to obtain and maintain a suitability or determination of eligibility for a Confidential/Secret or Top Secret security clearance as a condition of your employment
United States Citizenship

Nice To Haves

Healthcare industry experience, including claims, clinical, FHIR, HL7, or provider data
Experience with containerization (Docker, Kubernetes) for data workloads
Experience supporting machine learning workflows or analytical data science pipelines
Knowledge of distributed computing concepts and performance tuning

Responsibilities

Design, develop, and maintain scalable data pipelines using Python, PySpark, and other modern programming languages to support both batch and streaming workloads
Build and optimize data processing frameworks on cloud platforms such as Databricks or Snowflake, ensuring performance, reliability, and cost efficiency
Design and implement robust data models, including transactional (OLTP) and dimensional (OLAP) schemas, to support analytics, reporting, and application integration
Develop high quality SQL code including complex queries, stored procedures, and views, with a focus on performance tuning and efficient data access patterns
Create and manage workflow orchestration using Apache Airflow or similar tools, ensuring reliable scheduling, dependency management, and monitoring
Implement and enforce data governance and metadata standards through tools such as Microsoft Purview, including data lineage, classification, cataloging, and security policies
Build automated data quality and validation frameworks to ensure accuracy, completeness, and reliability of production datasets
Collaborate with cross functional teams including data architects, analysts, scientists, and business stakeholders to understand requirements and deliver scalable, well designed data solutions
Lead technical design sessions and code reviews, promoting engineering best practices, reusability, and maintainability
Support cloud infrastructure and DevOps practices, including CI/CD pipelines, version control, testing automation, and environment management
Monitor and troubleshoot production data pipelines, proactively addressing issues, performance bottlenecks, and system failures
Contribute to the evolution of the enterprise data platform, recommending tools, frameworks, and architectures to improve scalability and efficiency