> Markdown version of [/jobs/ext/4616434-principal-data-engineering-remote](https://www.wearedevelopers.com/jobs/ext/4616434-principal-data-engineering-remote). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Data Engineering - Remote - **Company:** Optum, Inc - **Location:** Minnetonka, MN, United States (Remote available) - **Experience:** Expert - **Salary:** $112,700 - $193,200 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Batch Processing, Cloud Computing, Cloud Storage, Continuous Integration, Data Architecture, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Identity and Access Management, Python (Programming Language), Azure Machine Learning, SQL Databases, Data Streaming, Enterprise Data Management, Data Logging, Data Storage Technologies, Feature Engineering, Cloud Monitoring, Great Expectations (Foster Youth College-readiness and Support Program), Snowflake, Apache Spark, Pyspark, Information Technology, Real Time Data, Machine Learning Operations, Terraform, Software Version Control, Data Pipelines, Databricks - **Published:** November 7, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=4766a23dcefedaec ## About the Role * 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 (GCP, 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 GCP 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, * Solid preference for Minnesota based candidates * Flexibility for U.S. based candidates if local talent is not available ## Description * 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 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.