Senior Data Engineer

Optum is a global organization that delivers care aided by technology to help millions of people live healthier lives. The Senior Data Engineer is responsible for designing, building, and operating scalable data platforms to enable analytics, reporting, and AI/ML, while ensuring data quality and governance.

Responsibilities:

• Design, build, and operate scalable ETL/ELT pipelines for structured and semi‑structured data across internal and external sources
• Develop cloud‑native data solutions on modern data platforms (e.g., Databricks lakehouse, Snowflake)
• Apply data modeling, transformations, and enrichment to produce trusted, analytics‑ready datasets
• Ensure data quality, reliability, and auditability through validation, reconciliation, and monitoring
• Implement and follow enterprise data governance, security, and compliance standards
• Optimize data workloads for performance, cost efficiency, and operational reliability
• Support modernization efforts, including migration of legacy ETL pipelines to cloud‑native architectures
• Partner with product, analytics, and business stakeholders to translate requirements into production‑grade data solutions
• Enable downstream analytics, reporting, and AI/ML workloads through well‑designed data assets
• Apply software engineering best practices, including CI/CD, version control, testing, and code reviews
• Contribute to architectural discussions and mentor junior engineers
• Design, develop, and deploy AI-powered solutions to address complex business challenges with emphasis on responsible use of AI

Requirements:

• Bachelor's degree in Computer Science, Engineering related field
• 6+ years of experience as a Data Engineer or in data platform / analytics engineering roles
• 5+ years of experience with SQL, data modeling, and data transformations
• 5+ years of hands‑on experience with Databricks and/or Snowflake and large‑scale data processing
• 5+ years of experience building ETL/ELT pipelines, data warehouses, and analytical data stores
• 5+ years of experience with source control and DevOps practices (Azure DevOps, GitHub, CI/CD)
• Working knowledge of cloud architecture, scalability, and fault‑tolerant design
• Familiarity with data quality frameworks and enterprise data ecosystems
• Experience with lakehouse patterns (e.g., medallion/Bronze‑Silver‑Gold architectures)
• Proficiency in Spark and Python for data engineering workloads
• Experience with data governance, metadata, and lineage tools
• Exposure to AI/ML or advanced analytics enablement through curated data assets
• Experience migrating legacy data platforms to modern cloud environments
• Prior experience mentoring engineers or contributing to engineering standards and best practices