Data Engineer

Optum is a global healthcare organization that uses technology to improve health outcomes and connect people with essential care, pharmacy benefits, and data. The Data Engineer will architect, build, and maintain enterprise ETL/ELT pipelines, integrate on-premises and cloud data, implement data governance and quality controls, and develop AI-powered solutions while collaborating with Agile teams.

Responsibilities:

• Design, build, and maintain enterprise ETL/ELT data pipelines integrating data from on-premises and cloud platforms into unified data repositories using Snowflake and Apache Airflow
• Develop complex data transformation routines, SQL stored procedures, and Python scripts to clean, normalize, and aggregate high-volume datasets
• Implement data de-identification, row-level security, and data masking mechanisms in accordance with enterprise data governance standards
• Establish automated data quality validation, monitoring, and error-handling routines using tools such as Great Expectations or Soda
• Create logical and physical data models (e.g., dimensional modeling, star schemas, Data Vault) to optimize analytics performance and data integrity
• Support CI/CD pipeline automation and deployment for data engineering workflows using Git and GitHub Actions
• Optimize data pipeline performance, query execution times, and resource utilization in cloud data platforms
• Collaborate within Agile/Scrum cross-functional teams to translate business requirements into scalable technical data architectures
• Design, develop, and deploy AI-powered solutions to address complex business challenges with emphasis on responsible use of AI

Requirements:

• 3+ years Proven hands-on experience in a Data Engineer role building scalable ETL/ELT data pipelines
• 2+ years' Experience with Azure cloud services (Blob Storage, Azure Data Factory, Azure Functions, Key Vault) or Databricks
• 2+ Experience with version control and CI/CD deployment pipelines using Git and GitHub Actions
• 1+ Proficiency in Python for data processing, scripting, and pipeline automation
• Bachelor's degree or higher in Computer Science, Information Technology, Database Management, or a related field
• In-depth knowledge of Snowflake architecture, performance tuning, data masking, and access control policies
• Experience orchestrating Airflow data tasks to run on Kubernetes and creating Docker containers
• Strong understanding of data modeling techniques (e.g., Star Schema, Dimensional Modeling, Data Vault) and data warehousing principles
• Experience using data quality frameworks such as Great Expectations or Soda
• Familiarity with open-source languages and environments such as Scala, Java, or Linux in Agile/DevOps workflows
• Experience orchestrating data workflows using Apache Airflow