Principal Data Engineering - Remote

UnitedHealth Group•Minnetonka, MN

1d•$112,700 - $193,200•Remote

About The Position

The Principal Data Engineer is a senior individual contributor responsible for designing, building, and scaling enterprise data platforms and pipelines. The role combines cloud-based data architecture ownership with hands-on technical work across cloud and hybrid environments, supporting large-scale data, analytics, and machine learning initiatives. Within Optum Technology, the engineer partners with engineering, BI, Data Science, and product teams to deliver reliable, performant, and secure data solutions. The role uses enterprise-approved AI tools and platforms such as Databricks, Snowflake, and GCP, Azure, or AWS to automate workflows, optimize pipelines, and advance the platform. The position is remote within the U.S., with employees hired in the Minneapolis or Washington, D.C. area required to work in the office at least four days per week. There is a strong preference for Minnesota-based candidates, with flexibility for U.S.-based candidates if local talent is unavailable. Remote employees must follow UnitedHealth Group’s Telecommuter Policy.

Requirements

At least 7 years of experience in data engineering, enterprise data platform development, or data architecture roles.
At least 5 years of hands-on experience building and optimizing scalable ELT/ETL pipelines using Python, SQL, and Spark/PySpark.
At least 3 years of experience developing data models and processing workflows on modern platforms such as Databricks or Snowflake.
At least 3 years of hands-on experience with GCP, Azure, or AWS, including cloud storage, IAM security controls, and hybrid architectures.
At least 3 years of experience building CI/CD workflows for data pipelines and managing Infrastructure-as-Code (IaC) using Terraform.
At least 3 years of experience implementing batch and streaming/micro-batch processing for near-real-time data use cases.
At least 3 years of experience building and operationalizing ML data pipelines, including feature engineering, training/inference data flows, and automated model retraining or scoring workflows.

Nice To Haves

Bachelor’s degree in Computer Science, Data Engineering, Information Technology, or a related technical field, or equivalent experience.
Relevant cloud or data certifications, such as Databricks Certified Data Engineer, Snowflake Certified, or GCP Professional Data Engineer.
Experience with data quality, observability, and data catalog tools such as Great Expectations, dbt tests, Cloud Monitoring, or Data Catalog.
Experience in healthcare, contact-center analytics, or highly regulated enterprise environments.

Responsibilities

Lead the design and implementation of cloud-based and hybrid data architectures, platforms, and enterprise data solutions.
Build, optimize, and maintain scalable ELT/ETL pipelines using Python, SQL, Spark/PySpark, and platforms such as Databricks and Snowflake.
Design, develop, and deploy AI-powered solutions and automated data engineering workflows for complex business challenges, emphasizing responsible AI practices.
Design and operationalize machine-learning pipelines with Data Science, including feature engineering, training and inference data flows, model versioning, and automated retraining or scoring workflows.
Use enterprise-approved AI tools to streamline technical workflows, automate data tasks, and drive continuous platform improvement.
Evaluate data engineering trends, cloud technologies, and AI capabilities to inform solution design and innovation.
Partner with Analytics/BI, Data Science, and Product teams to deliver trustworthy, high-performing datasets and metric frameworks.
Ensure data quality, observability, lineage, and documentation across pipelines using tools such as dbt, Great Expectations, and cloud logging and monitoring.
Implement security, IAM, encryption, PII handling, and regulatory compliance controls across data storage and pipeline layers.
Provide hands-on technical leadership, conduct solution design reviews, and mentor engineers.
Troubleshoot complex data pipeline incidents, lead root cause analysis, and implement preventive platform improvements.