Principal Data Engineer- Remote or Hybrid in MN or DC

UnitedHealth Group•Eden Prairie, MN

4d•$112,700 - $193,200•Hybrid

About The Position

Grow your career with an exciting opportunity with Optum, where you will be a part of creating software solutions that help to change lives - millions of lives. As a Data Engineer at Optum, you will have the opportunity to be a member of a focused team dedicated to helping to make the health care system work better for everyone. Here, you will partner with some of the smartest people you have ever worked with to design solutions to meet a wide range of health consumer needs. 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.

Requirements

Bachelor’s degree in engineering or equivalent experience
5+ years of experience designing and implementing cloud-based data solutions (Azure, Google, or Amazon)
5+ years of experience with modern data and platform technologies, including (or equivalent): Azure Databricks, Apache Kafka (or equivalent streaming platforms), Snowflake, GitHub and GitHub Actions
2+ years of experience with hands-on Python and/or Scala for data engineering workloads
Solid proficiency in SQL for data querying and transformation, along with a solid understanding of version control, CI/CD pipelines, and automated deployment practices for data platforms
Proven ability to collaborate effectively within Agile, distributed, and onshore/offshore teams

Nice To Haves

Relevant cloud certifications, particularly Microsoft Azure certifications such as Azure Data Engineer Associate or Azure Solutions Architect Expert
Experience within the healthcare industry
Firsthand experience with specific AI techniques and frameworks, such as Large Language Models (LLMs), Retrieval Augmented Generation (RAG), or autonomous agents
Working knowledge of RESTful APIs and data integration patterns
Thorough understanding of data modeling techniques (conceptual, logical, and physical) and deep knowledge of data warehousing architectures and best practices
Proven excellent analytical and problem-solving skills, with the ability to think creatively and deliver innovative data solutions in collaboration with delivery teams

Responsibilities

Partner closely with product owners, system analysts, and engineering teams to deliver data solutions aligned with an Agile roadmap
Collaborate with data architects and platform engineers to translate functional and non-functional requirements into scalable, high-quality data architecture
Serve as a technical leader and key point of contact for scrum teams, guiding and influencing solutions across teams and mentoring junior engineers
Design, build, test, and operate data-intensive systems, including streaming and batch pipelines using technologies such as Databricks, Kafka, Snowflake, and cloud-native storage solutions
Develop and maintain ETL/ELT pipelines, data transformations, and orchestration workflows using Python, SQL, and distributed processing frameworks
Implement and continuously improve CI/CD, DevOps, and DataOps practices to enable reliable, automated deployments of data pipelines and analytics workloads
Build and maintain configuration management, infrastructure automation, deployment strategies, and monitoring/observability tools for data platforms
Apply software and data engineering best practices, including reliability engineering, fault-tolerant architecture, performance tuning, and automated testing
Participate in incident management processes, root cause analysis, and remediation using tools such as ServiceNow
Define and execute data quality, governance, security, and resilience strategies to ensure trusted and compliant data products
Support data solutions throughout the entire SDLC, from design and development through production deployment and operational support
Participate in design reviews, architecture discussions, code reviews, defect triage, and performance optimization efforts
Adhere to established data modeling, coding, and platform standards, while continuously improving how data solutions are built and delivered