Note: The job is a remote job and is open to candidates in USA. Optum Technology, part of UnitedHealth Group, is hiring a Principal Data Engineer to design, build, and scale enterprise data platforms and pipelines across cloud and hybrid environments. The role supports data analytics and machine learning initiatives by improving data reliability, performance, security, and workflow automation.
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 data pipelines utilizing Python, SQL, Spark/PySpark, and modern data platforms (e.g., Databricks, Snowflake)
- Design, develop, and deploy AI-powered solutions and automated data engineering workflows to address complex business challenges with an emphasis on responsible AI practices
- Design and operationalize ML pipelines in partnership with Data Science, covering feature engineering, training and inference data flows, model versioning, and automated retraining or scoring workflows
- Leverage enterprise-approved AI tools to streamline technical workflows, automate data tasks, and drive continuous platform improvement
- Evaluate emerging data engineering trends, cloud technologies, and AI capabilities to inform solution design and strategic 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 data pipelines using tools like dbt, Great Expectations, and cloud logging/monitoring
- Implement security, IAM, encryption, PII handling, and regulatory compliance controls across all data storage and pipeline layers
- Provide hands-on technical leadership, solution design reviews, and mentorship to junior and senior engineers
- Troubleshoot and resolve complex data pipeline incidents, driving root cause analysis and preventive platform enhancements
Skills
- 7+ years of experience in data engineering, enterprise data platform development, or data architecture roles
- 5+ years of hands-on experience building and optimizing scalable ELT/ETL data pipelines using Python, SQL, and Spark/PySpark
- 3+ years of experience developing data models and processing workflows on modern data platforms such as Databricks or Snowflake
- 3+ years of hands-on experience with major cloud platforms (Google Cloud Platform, Azure, or AWS) including cloud storage, IAM security controls, and hybrid architectures
- 3+ years of experience building CI/CD workflows for data pipelines and managing Infrastructure-as-Code (IaC) using Terraform
- 3+ years of experience implementing batch and streaming/micro-batch processing solutions for near-real-time data use cases
- 3+ years of experience building and operationalizing ML data pipelines, including feature engineering, training/inference data flows, and automated model retraining or scoring workflows (e.g., MLflow, Databricks ML, Vertex AI, Azure ML, or SageMaker)
- Bachelor's degree in Computer Science, Data Engineering, Information Technology, or a related technical field (or equivalent experience)
- Relevant cloud or data certifications (e.g., Databricks Certified Data Engineer, Snowflake Certified, or Google Cloud Platform Professional Data Engineer)
- Experience with data quality, observability, and data catalog tools (e.g., Great Expectations, dbt tests, Cloud Monitoring, Data Catalog)
- Experience working in healthcare, contact-center analytics, or highly regulated enterprise environments
Benefits
- A comprehensive benefits package, subject to eligibility requirements
- Incentive and recognition programs, subject to eligibility requirements
- Equity stock purchase, subject to eligibility requirements
- 401k contribution, subject to eligibility requirements
- Flexibility to work remotely from anywhere within the U.S.; hires in the Minneapolis or Washington, D.C. area are required to work in the office a minimum of four days per week
Company Overview
Company H1B Sponsorship